System and method for physiological parameter monitoring

ABSTRACT

A device, method and system for calculating, estimating, or monitoring the blood pressure of a subject. A first signal representing heart activity of a subject may be received. A plurality of second signals representing time-varying information on at least one pulse wave of the subject may be received from a plurality of body locations of the subject. A first feature of the first signal may be identified. For each of the plurality of second signals, a second feature may be identified. A pulse transit time based on a difference of the first feature and at least one of the second features may be computed. A blood pressure of the subject may be calculated according to a model based on the computed pulse transit time. The model may include a compensation term relating to the plurality of second signals or the second features thereof.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a U.S. national phase entry of InternationalApplication No. PCT/CN2015/096498, filed on Dec. 5, 2015, which claimspriority to International Application No. PCT/CN2015/083334 filed Jul.3, 2015, which claims priority to Chinese Patent Application No.201520188152.9 filed Mar. 31, 2015. Each of the above-referencedapplications is incorporated herein by reference to their entireties.

TECHNICAL FIELD

The present disclosure generally relates to a system and methodapplicable in health-care related areas. More particularly, the presentdisclosure relates to a system and method for blood pressure monitoring.

BACKGROUND

A traditional blood pressure measurement system, also calledsphygmomanometers, employs Korotkoff sounds or an oscillometric methodto determine blood pressure based on the relationship of the externalpressure and magnitude of arterial volume pulsations. Such a traditionalblood pressure measurement system involves an inflatable cuff torestrict blood flow. Various cuff-based methods work discontinuouslywith an interval of some minutes or longer between consecutivemeasurements. Currently, ambulatory blood pressure measurement and homeblood pressure measurement are recommended by professional societies forhypertension management and cardiovascular risk prediction. However,such intermittent blood pressure measurements cannot capture the dynamicstate of cardiovascular system throughout a day or even longer timeperiod. Continuous and non-invasive blood pressure monitoring may allowthe investigation of transient changes in blood pressure and thus maygive insights into mechanisms of blood pressure control. There is a needfor a system and method to monitor blood pressure continuously in anon-invasive and cuffless way with certain accuracy.

SUMMARY

Some embodiments of the present disclosure relates to a device includingmemory storing instructions, and at least one processor. The device maybe used to calculate, estimate, or monitor the blood pressure of asubject. When the at least one processor executing the instructions, theat least one processor may perform one or more of the followingoperations. A first signal representing heart activity the subject, orfirst information relating to or representing the first signal, may bereceived. A plurality of second signals representing time-varyinginformation on at least one pulse wave of the subject, or secondinformation relating to or representing the second signal, may bereceived. The second signals or the second information may be from aplurality of body locations of the subject (or referred to as locationsof the body of the subject, or locations of the subject). A firstfeature of the first signal may be identified. The identification of thefirst feature in the first signal may be achieved by analyzing the firstinformation or the first signal. For each of the plurality of secondsignals, a second feature may be identified. The identification of thesecond feature of one of the plurality of second signals may be achievedby analyzing the second information or the second signal. A pulsetransit time based on a difference of the first feature and at least oneof the second features may be computed. In some embodiments, a bloodpressure of the subject may be calculated according to a model based onthe computed pulse transit time. The model may include a compensationterm relating to the plurality of second signals or the second featuresthereof.

Some embodiments of the present disclosure relates to a methodimplemented on at least one processor for calculating, estimating, ormonitoring the blood pressure of a subject. The method may include oneor more of the following operations. A first signal representing heartactivity the subject, or first information relating to or representingthe first signal, may be received. A plurality of second signalsrepresenting time-varying information on at least one pulse wave of thesubject, or second information relating to or representing the secondsignal, may be received. The second signals or the second informationmay be from a plurality of body locations of the subject (or referred toas locations of the body of the subject, or locations of the subject). Afirst feature of the first signal may be identified. The identificationof the first feature in the first signal may be achieved by analyzingthe first information or the first signal. For each of the plurality ofsecond signals, a second feature may be identified. The identificationof the second feature of one of the plurality of second signals may beachieved by analyzing the second information or the second signals. Apulse transit time based on a difference of the first feature and atleast one of the second feature may be computed. In some embodiments, ablood pressure of the subject may be calculated according to a modelbased on the computed pulse transit time. The model may include acompensation term relating to the plurality of second signals or thesecond features thereof.

Some embodiments of the present disclosure relates to a systemimplemented on memory and at least one processor. The system may be usedto calculate, estimate, or monitoring the blood pressure of a subject.The system may include an acquisition module and an analysis module. Theacquisition module may be configured to receive a first signalrepresenting heart activity of a subject (or first information relatingto or representing the first signal), and a plurality of second signalsrepresenting time-varying information on at least one pulse wave of thesubject (or second information relating to or representing the secondsignal). The second signals or the second information may be from aplurality of body locations of the subject (or referred to as locationsof the body of the subject, or locations of the subject). The analysismodule may be configured to identify a first feature in the firstsignal; identify, for each of the plurality of second signals, a secondfeature; compute a pulse transit time based on a difference between thefirst feature and one of the second features; and calculate a bloodpressure of the subject according to a model based on the computed pulsetransit time. The model may include a compensation term relating to theplurality of second signals or the second features thereof. Theidentification of the first feature in the first signal may be achievedby analyzing the first information or the first signal. Theidentification of the second feature in the plurality of second signalsmay be achieved by analyzing the second information or the secondsignals. The system may further include an output module configured toprovide the calculated blood pressure for output.

In some embodiments, a plurality of pulse transit time values may becomputed. The value of pulse transit time may be computed based on thedifference between the first feature of a first signal and the secondfeature of a second signal. The second signals may be from differentlocations of the body of a same subject. The plurality of pulse transittime values may be based on a same first signal or different firstsignals. For instance, two pulse transit time values may be based on onefirst signal and two second signals from two locations of the body ofthe subject. As another example, two pulse transit time values may bebased on two different first signals and two second signals from twolocations of the body of the subject. As used herein, different signalsmay be acquired from the same location of the body of a subject atdifferent times, or acquired from different locations of the body of asubject approximately the same time or at different times. For instance,different first signals or different second signals may be acquired fromthe same location of the body of the subject at different times. Asanother example, different first signals or different second signals maybe acquired from different locations of the body of a subjectapproximately the same time or at different times. In some embodiments,a plurality of blood pressure values of the subject may be calculatedaccording to a model based on the computed pulse transit time values. Insome embodiments, a plurality of blood pressure values of the subjectmay be calculated according to multiple models based on the computedpulse transit time values. Such models may be selected based on thelocation(s) where the first signal or the second signal whose feature(s)is/are used to compute the pulse transit time have been acquired. Forinstance, second signals are acquired from an upper arm and at an ankleof the subject, the second features of these two second signals areidentified, and two pulse transit time values are computed based on thetwo second features and a first feature of a same first signal; twoblood pressure values may be calculated based on the two pulse transittime values according to a same model, or according to two differentmodels; the two models may be selected based on the locations (the upperarm and the ankle) where the two second signals have been acquired.

In some embodiments, receiving the first signal may includecommunicating with a first sensor configured to acquire the first signalat a first location on the body of the subject. Receiving the firstsignal may include measuring or acquiring the first signal using a firstsensor configured to acquire the first signal at a first location on thebody of the subject. The first sensor may be part of the device. In someembodiments, the receiving the plurality of second signals may includecommunicating with a plurality of second sensors configured to acquirethe plurality of second signals at a plurality of second locations onthe body of the subject. In some embodiments, the receiving theplurality of second signals may include communicating with a pluralityof second sensor arrays configured to acquire the plurality of secondsignals at a plurality of second locations on the body of the subject.The second sensors or the second sensor arrays may be a part of thedevice. The first location and one of the second locations may besubstantially the same. The second locations may include head, the neck,the chest, the abdomen, the upper arm, the wrist, the waist, the upperleg, the knee, or the ankle of the subject. The device may include astructure that allows the device to be worn by the subject.

In some embodiments, at least one of the plurality of second sensorarrays may include a plurality of sensors, a plurality of receiving endsof a one or more sensors, or a plurality of emitting ends of one or moresensors. In some embodiments, the configuration of the sensor array maybe an oval array, a rectangular array, a circular array or a triangulararray.

In some embodiments, the first signal may include an optical signal oran electrical signal. The second signal may include an optical signal oran electrical signal. The first signal or the second signal may includea photoplethysmography (PPG) waveform, an electrocardiography (ECG)waveform, or a ballistocardiogram (BCG) waveform.

In some embodiments, a plurality of blood pressures of the plurality ofsecond locations may be calculated. In some embodiments, relationinformation may be generated. For example, the relation information mayinclude a distribution of blood pressure values of a plurality of bodylocations (for example, the left ankle versus the right ankle, the leftupper arm versus the right upper arm) of the subject. As anotherexample, the relation information may include a comparison of the bloodpressure value at a body location with a reference value. Exemplaryreference values may include one or more historic blood pressure valuesat the same or similar location of the subject, of a sub-group of peoplewho share a same or similar characteristic with the subject, or ageneral population. In some embodiments, the pulse transit time valueson the basis of which the blood pressure values are calculated may beused, instead of the blood pressure values themselves, to generate therelation information. In some embodiments, a recommendation relating tothe subject may be provided based on the relation information. In someembodiments, the recommendation may include a compensation termregarding the model to be used to calculate blood pressure. In someembodiments, the recommendation may be a prompting message regardingselection of a model that may be used to calculate blood pressure. Insome embodiments, the recommendation may include a model that may beused to calculate blood pressure. The model may include the modifiedfirst model by the compensation term. In some embodiments, therecommendation may be a push information regarding daily activities ofthe subject.

In some embodiments, the at least one processor may further receiveinformation relating to the subject or a condition when the first signalor the second signal is acquired. Exemplary information may include,e.g., age, body weight, the time (during the day) or the date the firstsignal or the second signal is acquired, the room temperature, the moodof the subject at the time, whether the subject has recently exercised,or the like, or a combination thereof. Such information may be takeninto consideration when the blood pressure of the subject is calculatedusing the device.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 illustrates an exemplary system configuration in which a systemfor monitoring a physiological signal may be deployed in accordance withvarious embodiments of the present disclosure;

FIG. 2 depicts an exemplary diagram of an engine of the systemillustrated in FIG. 1, according to some embodiments of the presentdisclosure;

FIG. 3 is a flowchart of an exemplary process in which a method forestimating a physiological signal is deployed, according to someembodiments of the present disclosure;

FIG. 4 is a block diagram illustrating an architecture of an acquisitionmodule according to some embodiments of the present disclosure;

FIG. 5-A and FIG. 5-B are schematic diagrams showing the arrangement orthe location of the ECG sensors and PPG sensors according to someembodiments of the present disclosure;

FIG. 6-A through FIG. 6-D are schematic diagrams showing exemplarysensor arrays of PPG sensors according to some embodiments of thepresent disclosure;

FIG. 7 is a flowchart of a process for estimating a physiologicalparameter of interest according to some embodiments of the presentdisclosure;

FIG. 8-A through FIG. 8-E provide exemplary signal processing accordingto some embodiments of the present disclosure;

FIG. 9 illustrates an exemplary personal health manager according tosome embodiments of the present disclosure;

FIG. 10 depicts the architecture of a mobile device that may be used toimplement a specialized system or a part thereof incorporating thepresent disclosure;

FIG. 11 depicts the architecture of a computer that may be used toimplement a specialized system or a part thereof incorporating thepresent disclosure; and

FIG. 12 illustrates an exemplary device according to some embodiments ofthe present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure.

The present disclosure relates to system, method, and programmingaspects of blood pressure monitoring. The blood pressure monitoring mayinvolve a cuffless system and method. In some embodiments, bloodpressure is estimated based on pulse wave related information, forexample, pulse transit time (PTT), pulse arrival time (PAT), or thelike, or a combination thereof. The system and method involve improvedsensor design and signal processing. The system and method as disclosedherein may perform blood pressure monitoring continuously in anon-invasive way, with improved accuracy. The system and method asdisclosed herein may perform blood pressure monitoring of multiple bodylocations in real time. The following description is provided withreference to PTT in connection with the blood pressure monitoring forillustration purposes, and is not intended to limit the scope of thepresent disclosure. Merely by way of example, the system and method asdisclosed herein may utilize one or more other pulse wave relatedinformation or signals, for example, PAT, for blood pressure monitoring.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawing(s), allof which form a part of this specification. It is to be expresslyunderstood, however, that the drawing(s) are for the purposes ofillustration and description only and are not intended to limit thescope of the present disclosure. As used in the specification and in theclaims, the singular form of “a,” “an,” and “the” include pluralreferents unless the context clearly dictates otherwise.

FIG. 1 illustrates an exemplary system configuration in which a system100 may be deployed in accordance with some embodiments of the presentdisclosure. The system 100 may be configured to monitor a physiologicalparameter of interest. The system 100 may include a measuring device110, a database (for example, a server 120), an external data source130, and a terminal 140. Various components of the system 100 may beconnected to each other directly or indirectly via a network 150.

The measuring device 110 may be configured to measure a signal. Thesignal may be a cardiovascular signal. The signal may relate to or beused to calculate or estimate a physiological parameter of interest. Themeasuring device 110 may include, for example, a clinical device, ahousehold device, a portable device, a wearable device, or the like, ora combination thereof. As used herein, a clinical device may be one thatmeets applicable standards and/or specifications to be used in aclinical setting including, for example, a hospital, a doctor's office,a nursing home, or the like. A clinical device may be used by or withthe assistance of a healthcare provider. As used herein, a householddevice may be one that meets applicable standards and/or specificationsto be used at home or a nonclinical setting. A household device may beused by someone who is or is not a professional provider. A clinicaldevice or a household device, or a portion thereof, may be portable orwearable. Exemplary clinical devices include an auscultatory device, anoscillometric device, an ECG monitor, a PPG monitor, or the like, or acombination thereof. Exemplary household devices include anoscillometric device, a household ECG monitor, a sphygmometer, or thelike, or a combination thereof. Exemplary portal devices include anoscillometric device, a portable ECG monitor, a portable PPG monitor, orthe like, or a combination thereof. Exemplary wearable devices include apair of glasses 111, a shoulder strap 112, a smart watch 113, an anklet114, a thigh band 115, an armband 116, a chest belt 117, a necklet 118,or the like, or a combination thereof. The above mentioned examples ofmeasuring devices 110 are provided for illustration purposes, and notintended to limit the scope of the present disclosure. A measuringdevice 110 may be in another form including, for example, a fingerstall,a wristband, a brassiere, an underwear, a chest band, or the like, or acombination thereof.

Merely by way of example, the measuring device 110 is a wearable orportable device configured to measure one or more cardiovascularsignals. In some embodiments, the wearable or portable device mayprocess at least some of the measured signals, estimate a physiologicalparameter of interest based on the measured signals, display a resultincluding the physiological parameter of interest in the form of, forexample, an image, an audio alert, perform wired or wirelesscommunication with another device or server (for example, the server120), or the like, or a combination thereof. In some embodiments, thewearable or portable device may communicate with another device (forexample, the terminal 140) or a server (for example, a cloud server).The device or server may process at least some of the measured signals,estimate a physiological parameter of interest based on the measuredsignals, display a result including the physiological parameter ofinterest in the form of, for example, an image, an audio alert, or thelike, or a combination thereof.

In some embodiments, the operations of processing the measured signals,estimating a physiological parameter, displaying a result, or performingwired or wireless communication may be performed by an integrated deviceor by separate devices connected to or communicating with each other.Such an integrated device may be portable or wearable. In someembodiments, at least some of the separate devices may be portable orwearable, or located in the vicinity of a subject whose signal ismeasured or a physiological parameter of interest is estimated ormonitored. Merely by way of example, the subject wears the measuringdevice 110 that is configured to measure one or more cardiovascularsignals; the measured one or more cardiovascular signals are transmittedto a smart phone that is configured to calculate or estimate aphysiological parameter of interest based on the measured signals. Insome embodiments, at least some of the separate devices are located in alocation remote from the subject. Merely by way of example, the subjectwears the measuring device 110 that is configured to measure one or morecardiovascular signals; the measured one or more cardiovascular signalsare transmitted to a server that is configured to calculate or estimatea physiological parameter of interest based on the measured signals; thecalculated or estimated physiological parameter of interest may betransmitted back to the subject, or a user other than the subject (forexample, a doctor, a care provider, a family member relating to thesubject, or the like, or a combination thereof).

In some embodiments, the measuring devices 110 may incorporate varioustypes of sensors including, for example, an electrode sensor, an opticalsensor, a photoelectric sensor, a pressure sensor, an accelerometer, agravity sensor, a temperature sensor, a moisture sensor, or the like, ora combination thereof. The measuring device may be configured to monitorand/or detect one or more types of variables including, for example,temperature, humidity, user or subject input, or the like, or acombination thereof. The measuring devices 110 may also incorporate apositioning system, for example, a GPS receiver, or a location sensor,and the position information may be transmitted to the server 120, theexternal data source 130, the terminal 140, or the like, or acombination thereof, through the network 150. The position informationand measured signals may be transmitted simultaneously or successively.

The system may include or communicate with a server or a databaseconfigured for storing a library 1100 and/or algorithms 121. The serveror database may be the server 120. The server 120 may be a cloud server.Merely by way of example, the server 120 may be implemented in a cloudserver that may provide storage capacity, computation capacity, or thelike, or a combination thereof. The library 1100 may be configured tocollect or store data. The data may include personal data, non-personaldata, or both. The data may include static data, dynamic data, or both.Exemplary static data may include various information regarding asubject including identity, contact information, birthday, a healthhistory (for example, whether a subject has a history of smoking,information regarding a prior surgery, a food allergy, a drug allergy, amedical treatment history, a history of genetic disease, a family healthhistory, or the like, or a combination thereof), the gender, thenationality, the height, the weight, the occupation, a habit (forexample, a health-related habit such as an exercise habit), theeducation background, a hobby, the marital status, religious belief, orthe like, or a combination thereof. Exemplary dynamic data may include acurrent health condition of a subject, medications the subject istaking, a medical treatment the subject is undertaking, diet,physiological signals or parameters (for example, pulse transit time(PTT), systolic blood pressure (SBP), diastolic blood pressure (DBP), orthe like) relating to the subject for multiple time points or over aperiod of time, or the like, or a combination thereof.

As used herein, a subject may refer to a person or animal whose signalor information is acquired and whose physiological parameter isacquired, estimated, or monitored. Merely by way of example, a subjectmay be a patient whose cardiovascular signals are acquired, and bloodpressure estimated or monitored based on the acquired cardiovascularsignals.

One or more algorithms 121 in the server 120 may be applied in dataprocessing or analysis, as described elsewhere in the presentdisclosure. The description of the server 120 above is provided forillustration purposes, and not intended to limit the scope of thepresent disclosure. The server 120 may have a different structure orconfiguration. For example, algorithms 121 are not stored in the server120; instead, the algorithms 121 may be stored locally at the terminal140. Furthermore, a library 1100 may also be stored at the terminal 140.

The external data sources 130 may include a variety of organizations,systems, and devices, or the like, or a combination thereof. Exemplarydata sources 130 may include a medical institution 131, a researchfacility 132, a conventional device 133, and a peripheral device 134, orthe like, or a combination thereof. The medical institution 131 or theresearch facility 132 may provide, for example, personal medicalrecords, clinical test results, experimental research results,theoretical or mathematical research results, algorithms suitable forprocessing data, or the like, or a combination thereof. The conventionaldevice 133 may include a cardiovascular signal measuring device, such asa mercury sphygmomanometer. A peripheral device 134 may be configured tomonitor and/or detect one or more types of variables including, forexample, temperature, humidity, user or subject input, or the like, or acombination thereof. The above mentioned examples of the external datasources 130 and data types are provided for illustration purposes, andnot intended to limit the scope of the present disclosure. For instance,the external data sources 130 may include other sources and other typesof data, such as genetic information relating to a subject or hisfamily.

The terminal 140 in the system 100 may be configured for processing atleast some of the measured signals, estimating a physiological parameterof interest based on the measured cardiovascular signals, displaying aresult including the physiological parameter of interest in the form of,for example, an image, storing data, controlling access to the system100 or a portion thereof (for example, access to the personal datastored in the system 100 or accessible from the system 100), managinginput-output from or relating to a subject, or the like, or acombination thereof. The terminal 140 may include, for example, a mobiledevice 141 (for example, a smart phone, a tablet, a laptop computer, orthe like), a personal computer 142, other devices 143, or the like, or acombination thereof. Other devices 143 may include a device that maywork independently, or a processing unit or processing module assembledin another device (for example, an intelligent home terminal). Merely byway of example, the terminal 140 includes a CPU or a processor in ameasuring device 110. In some embodiments, the terminal 140 may includean engine 200 as described in FIG. 2, and the terminal 140 may alsoinclude a measuring device 110.

The network 150 may be a single network or a combination of differentnetworks. For example, the network 150 may be a local area network(LAN), a wide area network (WAN), a public network, a private network, aproprietary network, a Public Telephone Switched Network (PSTN), theInternet, a wireless network, a virtual network, or any combinationthereof. The network 150 may also include various network access points,for example, wired or wireless access points such as base stations orInternet exchange points (not shown in FIG. 1), through which a datasource or any component of the system 100 described above may connect tothe network 150 in order to transmit information via the network 150.

Various components of or accessible from the system 100 may include amemory or electronic storage media. Such components may include, forexample, the measuring device 110, the server 120, the external datasources 130, the terminal 140, peripheral equipment 240 discussed inconnection with FIG. 2, or the like, or a combination thereof. Thememory or electronic storage media of any component of the system 100may include one or both of a system storage (for example, a disk) thatis provided integrally (i.e. substantially non-removable) with thecomponent, and a removable storage that may be removably connected tothe component via, for example, a port (for example, a USB port, afirewire port, etc.) or a drive (for example, a disk drive, etc.). Thememory or electronic storage media of any component of the system 100may include or be connectively operational with one or more virtualstorage resources (for example, cloud storage, a virtual privatenetwork, and/or other virtual storage resources).

The memory or electronic storage media of the system 100 may include adynamic storage device configured to store information and instructionsto be executed by the processor of a system-on-chip (SoC, for example, achipset including a processor), other processors (or computing units),or the like, or a combination thereof. The memory or electronic storagemedia may also be used to store temporary variables or otherintermediate information during execution of instructions by theprocessor(s). Part of or the entire memory or electronic storage mediamay be implemented as Dual In-line Memory Modules (DIMMs), and may beone or more of the following types of memory: static random accessmemory (SRAM), Burst SRAM or SynchBurst SRAM (BSRAM), dynamic randomaccess memory (DRAM), Fast Page Mode DRAM (FPM DRAM), Enhanced DRAM(EDRAM), Extended Data Output RAM (EDO RAM), Extended Data Output DRAM(EDO DRAM), Burst Extended Data Output DRAM (BEDO DRAM), Enhanced DRAM(EDRAM), synchronous DRAM (SDRAM), JEDECSRAM, PCIOO SDRAM, Double DataRate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), SyncLink DRAM (SLDRAM),Direct Rambus DRAM (DRDRAM), Ferroelectric RAM (FRAM), or any other typeof memory device. The memory or electronic storage media may alsoinclude read-only memory (ROM) and/or another static storage deviceconfigured to store static information and instructions for theprocessor of the SoC and/or other processors (or computing units).Further, the memory or electronic storage media may include a magneticdisk, optical disc or flash memory devices to store information andinstructions.

In some embodiments, the SoC may be part of a core processing orcomputing unit of a component of or accessible from the system 100. TheSoC may be configured to receive and process input data andinstructions, provide output and/or control other components of thesystem. In some embodiments, the SoC may include a microprocessor, amemory controller, a memory, and a peripheral component. Themicroprocessor may further include a cache memory (for example, SRAM),which along with the memory of the SoC may be part of a memory hierarchyto store instructions and data. The microprocessor may also include oneor more logic modules such as a field programmable gate array (FPGA) orother logic array. Communication between the microprocessor in the SoCand memory may be facilitated by the memory controller (or chipset),which may also facilitate in communicating with the peripheralcomponent, such as a counter-timer, a real-time timer, a power-on resetgenerator, or the like, or a combination thereof. The SoC may alsoinclude other components including, for example, a timing source (forexample, an oscillator, a phase-locked loop, or the like), a voltageregulator, a power management circuit, or the like, or a combinationthereof.

Merely by way of example, the system 100 may include a wearable orportable device. The wearable or portable device may include a SoC and aplurality of sensors. Exemplary sensors may include a photoelectricsensor, a conductance sensor, or the like, or a combination thereof. TheSoC may process signals acquired through at least some of the pluralityof sensors. The acquired signals may be various physiological signalsincluding, for example, photoplethysmograph (PPG), electrocardiograph(ECG), or the like, or a combination thereof. The SoC may calculate aphysiological parameter of interest based on the acquired signals.Exemplary physiological parameters of interest may be blood pressure, orthe like, or a combination thereof.

In some embodiments, the external data source 130 may receive data fromthe measuring device 110, the sever 120, the terminal 140, or the like,or any combination by the network 150. Merely by way of example, theexternal data source 130 (for example, a medical institution, or a smarthome system, or the like) may receive information relating to a subject(for example, location information, data from the cloud sever or aterminal, or the like, or a combination thereof) based on the datareceived from the measuring devices 110 or the terminals 140. In someembodiments, the measuring device 110 may receive data from the sever120, the external data source 130, or the like, or any combination, viathe network 150. Merely by way of example, the measuring device 110 mayreceive the information relating to a subject (for example, acurrent/historical health condition of a subject, medications thesubject is taking, medical treatment the subject is undertaking,current/historical diets, current emotion status, historicalphysiological parameters (for example, PTT, SBP, DBP) relating to thesubject, or the like, or a combination thereof). Furthermore, theterminal 140 may receive data from the measuring device 110, the server120, the external data source 130, or the like, or a combinationthereof.

FIG. 1 is a specific example of the system 100, and the configuration ofthe system 100 is not limited to that illustrated in FIG. 1. Forexample, a server 120 may be omitted, migrating all of its functions toa terminal 140. In another example, a server 120 and a terminal 140 mayboth be omitted, migrating all of their functions to a measuring device110. The system may include various devices or combinations of devicesin different embodiments.

In an example, the system may include a wearable or portable device anda mobile device (for example, a smart phone, a tablet, a laptopcomputer, or the like). The wearable or portable device may be used toacquire physiological signals, environmental information, or the like,or a combination thereof. The mobile device may be used to receive thesignals or information acquired by the wearable or portable device. Themobile device may calculate one or more physiological parameters ofinterest based on the acquired signals or information, as well asrelevant data retrieved from another source (for example, from aserver). The retrieved relevant data may include, for example,current/historical information stored on the server. Exemplarycurrent/historical information may include a current/historical healthcondition of a subject, current/historical medications the subjectis/was taking, current/historical medical treatment the subject is/wasundertaking, current/historical diets, current/historical emotionstatus, current/historical physiological parameters (for example, PTT,SBP, DBP) relating to the subject, or the like, or a combinationthereof. The wearable or portable device, or the mobile device maydisplay or report, or store at least some of the acquired signals,information, the retrieved relevant data, the calculated one or morephysiological parameters of interest, or the like, or a combinationthereof. The display or report may be provided to a subject, a userother than the subject, a third party, the server, or another device.

In another example, the system may include a wearable or portable devicethat may be configured to perform functions including: acquiringphysiological signals or environmental information, retrieving relevantdata from another source (for example, from a server), calculating oneor more physiological parameters of interest based on the acquiredsignals, information, or the retrieved relevant data, and displaying,reporting, or storing at least some of the acquired signals,information, the retrieved relevant data, the calculated one or morephysiological parameters of interest, or the like, or a combinationthereof. The display or report may be provided to a subject, a userother than the subject, a third party, the server, or another device.

In a further example, the system may include a wearable or portabledevice that may be configured to perform functions including: acquiringphysiological signals and environmental information, communicating witha server to transmit at least some of the acquired signals orinformation to the server such that the server may calculate one or morephysiological parameters of interest, receiving the calculated one ormore physiological parameters of interest from the server, displaying,reporting or storing at least some of the acquired signals, information,the calculated one or more physiological parameters of interest, or thelike, or a combination thereof. The display or report may be provided toa subject, a user other than the subject, a third party, the server, oranother device. In some embodiments, the communication between thewearable or portable device and the server may be achieved by way of thewearable or portable device being connected to a network (for example,the network 150). In some embodiments, the communication between thewearable or portable device and the server may be achieved via acommunication device (for example, a mobile device such as a smartphone, a tablet, a laptop computer, or the like) that communicates withboth the wearable or portable device and the server.

In still a further example, the system may include a wearable orportable device, a mobile device (for example, a smart phone, a tablet,a laptop computer, or the like), and a server. The wearable or portabledevice may be used to acquire physiological signals, environmentalinformation, or the like, or a combination thereof. The mobile devicemay be used to receive the signals or information acquired by thewearable or portable device, and may calculate one or more physiologicalparameters of interest based on the received signals and/or informationretrieved from the wearable or portable device, as well as relevant dataretrieved from another source (for example, a server). The mobile devicemay display, report, or store at least some of the acquired signals,information, the retrieved relevant data, the calculated one or morephysiological parameters of interest, or the like, or a combinationthereof. The display or report may be provided to a subject, a userother than the subject, a third party, the server, or another device.

In some embodiments, the system may be configured to provide a userinterface to allow a subject, a user other than the subject, or anentity to exchange information (including input into or output from thesystem) with the system as disclosed herein. The user interface may beimplemented on a terminal device including, for example, a mobiledevice, a computer, or the like, or a combination thereof. The access tothe system may be allowed to one who has an appropriate accessprivilege. An access privilege may include, for example, a privilege toread some or all information relating to a subject, update some or allinformation relating to a subject, or the like, or a combinationthereof. The access privilege may be associated with or linked to a setof login credentials. Merely by way of example, the system may providethree tiers of access privileges. A first tier may include a full accessprivilege regarding information relating to a subject, allowing bothreceiving and updating information relating to a subject. A second tiermay include a partial access privilege regarding information relating toa subject, allowing receiving and updating part of information relatingto a subject. A third tier may include a minimal access privilegeregarding information relating to a subject, allowing receiving orupdating part of information relating to a subject Different logincredentials may be associated with different access privilege to theinformation relating to a subject in the system. As used herein,updating may include providing information that does not exist in thesystem, or modifying pre-existing information with new information.

Merely by way of example, the system may receive information relating toa subject provided via the user interface. The information relating to asubject may include basic information and optional information.Exemplary basic information may include the height, the weight, the age(or the date of birth), the gender, the arm length, the nationality, theoccupation, a habit (for example, a health-related habit such as anexercise habit), the education background, a hobby, the marital status,religious belief, a health-related history (for example, whether asubject has a history of smoking, a food allergy, a drug allergy, amedical treatment history, a family health history, a history of geneticdisease, information regarding a prior surgery, or the like, or acombination thereof), contact information, emergency contact, or thelike, or a combination thereof. Exemplary optional information mayinclude, current health condition of the subject, medications thesubject is taking, a medical treatment the subject is undertaking, diet.The system may receive, via the user interface, information relating toa specific measurement of, for example, a physiological parameter ofinterest. Examples of such information may include the motion state ofthe subject at or around the acquisition time (defined elsewhere in thepresent disclosure), the emotional state at or around the acquisitiontime, the stress level at or around the acquisition time, or the like,or a combination thereof. The system may receive, via the userinterface, one or more options or instructions. In some embodiments, theoptions or instructions may be provided by a subject or a user otherthan the subject answering questions or making selections in response toquestions or prompts by the system. In one example, the options orinstructions may include a measurement frequency (for example, once aweek, once a month, twice a week, twice a month, once a day, twice aday, or the like), a preferred format of the presentation of informationto the subject or a user other than the subject (for example, email, avoice message, a text message, an audio alert, haptic feedback, or thelike, or a combination thereof). In another example, the options orinstructions may include information relating to calculating parametersof interest, for example, rules regarding how to select a model, afunction, calibration data, or the like, or a combination thereof.

In some embodiments, the system may provide, via the user interface,information to a subject, or a user other than the subject. Exemplaryinformation may include an alert, a recommendation, a reminder, or thelike, or a combination thereof. In one example, an alert may be providedor displayed to the subject or a user other than the subject if atriggering event occurs. Exemplary triggering events may be that atleast some of the acquired information or a physiological parameter ofinterest exceeds a threshold. Merely by way of example, a triggeringevent may be that the acquired heart rate exceeds a threshold (forexample, higher than 150 beats per minute, lower than 40 beats perminute, or the like). As another example, a triggering event may be thatthe physiological parameter of interest, for example, an estimated bloodpressure, exceeds a threshold. In another example, a recommendation maybe provided or displayed to the subject or a user other than thesubject. Exemplary recommendations may be a request to input specificdata (for example, basic information, optional information, updatedparameters of interest, updated models, updated functions, updatedoptions and instructions, or the like, or a combination thereof). Areminder may be provided or displayed to the subject or a user otherthan the subject. Exemplary reminders may include a reminder to take aprescription medication, take a rest, take a measurement of aphysiological parameter of interest, or the like, or a combinationthereof.

In some embodiments, the system may communicate with the subject, a userother than the subject, and/or a third party through the user interface.Exemplary third parties may be a doctor, a healthcare worker, a medicalinstitution, a research facility, a peripheral device of the subject ora user well-connected to the subject, or the like. Exemplarycommunications may relate to health conditions of the subject, a dietaryhabit, an exercise habit, a prescription medication, instructions orsteps to conduct a measurement, or the like, or a combination thereof.In some embodiments, a user interface accessible to or by a third partymay be the same as, or different from a user interface accessible to orby a subject. In one example, an output or data may be transmitted to athird party (for example, a computer, a terminal at a doctor's office, ahospital where a health care provider is located and the healthcondition of the subject is being monitored, or the like, or acombination thereof). The third party may provide feedback informationor instructions related to the output information via the userinterface. Merely by way of example, a third party may receiveinformation regarding one or more physiological parameters of interestrelating to a subject, and accordingly provide a recommendation ofactions to be taken by the subject (for example, to take a prescriptionmedication, to take a rest, to contact or visit the third party, or thelike, or a combination thereof); the system may relay the recommendationto the subject.

FIG. 2 shows an exemplary diagram including the engine 200. The engine200 may be configured for acquiring one or more signals and calculatingor estimating one or more physiological parameters of interest based onthe acquired signals. As illustrated, the engine 200 may be connected toor otherwise communicate with, for example, peripheral equipment 240,and the server 120. The engine 200 may include an informationacquisition module 210, an analysis module 220, and an output module230. The information acquisition module 210 may be configured foracquiring a signal or information relating to a subject, for example, aphysiological signal, information relating to the health condition ofthe subject, or the like, or a combination thereof. The analysis module220 may be configured for analyzing the acquired signal or information,or determining or estimating a physiological parameter of interest, orboth. The output module 230 may be configured for outputting theacquired signal or information, the physiological parameter of interest,or the like, or a combination thereof. As used herein, a module may havean independent processor, or use system shared processor(s). Theprocessor(s) may perform functions according to instructions related tovarious modules. For example, the analysis module 220, according torelevant instructions, may retrieve acquired signals and performcalculations to obtain one or more physiological parameter of interest.

The information acquisition module 210 may be configured for acquiring asignal or information from or relating to one or more subjects. As usedherein, acquiring may be achieved by way of receiving a signal orinformation sensed, detected, or measured by, for example, a sensor, orby way of receiving an input from a subject or from a user other thanthe subject (for example, a doctor, a care provider, a family memberrelating to the subject, or the like, or a combination thereof). Forbrevity, an acquired signal or information may be referred to asacquired information. As used herein, information may include a signalrelating to a subject that is acquired by a device including, forexample, a sensor, environmental information that is acquired by adevice including, for example, a sensor, information that is acquiredotherwise including, for example, from an input by a subject or a userother than the subject, a processed or pre-treated information that isacquired as described, or the like, or a combination thereof. Exemplarysensors may include an electrode sensor, an optical sensor, aphotoelectric sensor, a pressure sensor, an accelerometer, a gravitysensor, a temperature sensor, a moisture sensor, or the like, or acombination thereof.

Exemplary acquired information may include physiological information. Inthe exemplary context of determining blood pressure, the physiologicalinformation may include a cardiovascular signal. Exemplarycardiovascular signals may include a photoplethysmogram (PPG) signal, anelectrocardiogram (ECG) signal, a ballistocardiogram (BCG) signal, ablood pressure (BP), a systolic blood pressure (SBP), a diastolic bloodpressure (DBP), a pulse rate (PR), a heart rate (HR), a heart ratevariation (HRV), cardiac murmur, blood oxygen saturation, a density ofblood, a pH value of the blood, a bowel sound, a brainwave, a fatcontent, a blood flow rate, or the like, or a combination thereof.Exemplary acquired information may include information regarding asubject, for example, the height, the weight, the age, the gender, thebody temperature, the arm length, an illness history, or the like, or acombination thereof. Exemplary acquired information may includeinformation from or relating to the ambient surrounding a subject(referred to as environmental information) at or around the acquisitiontime. Exemplary environmental information may include temperature,humidity, air pressure, an air flow rate, an ambient light intensity, orthe like, or a combination thereof. As used herein, the acquisition timemay refer to a time point or a time period when information relating tothe subject, for example, physiological information of the subject, isacquired.

The information acquisition module 210 may be configured to receive orload information from the peripheral equipment 240, the server 120, orother devices (not shown) including, for example, an ECG monitor, a PPGmonitor, a respiratory monitor, a brainwave monitor, a blood glucosemonitor, and a device having similar functions. Examples of peripheralequipment 240 may include a smart watch, an earphone, a pair of glasses,a bracelet, a necklace, or the like, or a combination thereof. Theperipheral equipment 240, the server 120, or other devices may be localor remote. For example, the server 120 and the engine 200 may beconnected through a local area network (LAN), or Internet. Theperipheral equipment 240 and the engine 200 may be connected through alocal area network, or Internet. Other devices and the engine 200 may beconnected through a local area network, or Internet. The informationtransmission between the information acquisition module 210 and theperipheral equipment 240, the server 120, or such other devices may bevia a wired connection, a wireless connection, or the like, or acombination thereof.

The information acquisition module 210 may be configured to receiveinformation provided by a subject or a user other than the subject via,for example, an input device. The input device may include but is notlimited to a keyboard, a touch screen (for example, with haptics ortactile feedback), a speech input device, an eye tracking input device,a brain monitoring system, or the like, or a combination thereof. Theinformation received through the input device may be transmitted to aprocessor, via, for example, a bus, for further processing. Theprocessor for further processing the information obtained from the inputdevice may be a digital signal processor (DSP), a SoC (system on thechip), or a microprocessor, or the like, or the combination thereof.Other types of input device may include cursor control device, such as amouse, trackball, or cursor direction keys to convey information aboutdirection and/or command selections, for example, to the processor.

The description of the information acquisition module 210 is intended tobe illustrative, and not to limit the scope of the present disclosure.Many alternatives, modifications, and variations will be apparent tothose skilled in the art. The features, structures, methods, and othercharacteristics of the exemplary embodiments described herein may becombined in various ways to obtain additional and/or alternativeexemplary embodiments. For example, a storage unit (not shown in FIG. 2)may be added to the information acquisition module 210 for storing theacquired information.

The analysis module 220 may be configured for analyzing acquiredinformation. The analysis module 220 may be connected to or otherwisecommunicate with one or more information acquisition modules 210-1,210-2, . . . , 210-N to receive at least part of the acquiredinformation. The analysis module 220 may be configured for performingone or more operations including, for example, a pre-treatment, acalculation, a calibration, a statistical analysis, or the like, or acombination thereof. Any one of the operations may be performed based onat least some of the acquired information, or an intermediate resultfrom another operation (for example, an operation performed by theanalysis module 220, or another component of the system 100). Forinstance, the analysis may include one or more operations includingpre-treating at least part of the acquired information, identifying acharacteristic point or feature of the acquired information or thepre-treated information, calculating an intermediate result based on theidentified characteristic point or feature, performing a calibration,analyzing the information regarding the subject provided by the subjector a user other than the subject, analyzing the information regardingthe ambient environment surrounding the subject at or around theacquisition time, estimating a physiological parameter of interest, orthe like, or a combination thereof.

Some operations of the analysis may be performed in parallel or inseries. As used herein, a parallel performance may indicate that someoperations of the analysis may be performed at or around the same time;a serial performance may indicate that some operations of the analysismay commence or be performed after other operations of the analysis. Insome embodiments, at least two operations of an analysis may beperformed in parallel. In some embodiments, at least two operations ofan analysis may be performed in series. In some embodiments, some of theoperations of an analysis may be performed in parallel, and some of theoperations may be performed in series.

The analysis, or some operations of the analysis, may be performed realtime, i.e. at or around the acquisition time. The analysis, or someoperations of the analysis, may be performed after a delay since theinformation is acquired. In some embodiments, the acquired informationis stored for analysis after a delay. In some embodiments, the acquiredinformation is pre-treated and stored for further analysis after adelay. The delay may be in the order of seconds, or minutes, or hours,or days, or longer. After the delay, the analysis may be triggered by aninstruction from a subject or a user other than the subject (forexample, a doctor, a care provider, a family member relating to thesubject, or the like, or a combination thereof), an instruction storedin the system 100, or the like, or a combination thereof. Merely by wayof example, the instruction stored in the system 100 may specify theduration of the delay, the time the analysis is to be performed, thefrequency the analysis is to be performed, a triggering event thattriggers the performance of the analysis, or the like, or a combinationthereof. The instruction stored in the system 100 may be provided by asubject or a user other than the subject. An exemplary triggering eventmay be that at least some of the acquired information or a physiologicalparameter of interest exceeds a threshold. Merely by way of example, atriggering event may be that the acquired heart rate exceeds a threshold(for example, higher than 150 beats per minute, lower than 40 beats perminute, or the like). As used herein, “exceed” may be larger than orlower than a threshold. As another example, a triggering event may bethat the physiological parameter of interest, for example, an estimatedblood pressure, exceeds a threshold.

The analysis module 220 may be centralized or distributed. A centralizedanalysis module 220 may include a processor (not shown in FIG. 2). Theprocessor may be configured for performing the operations. A distributedanalysis module 220 may include a plurality of operation units (notshown in FIG. 2). The operation units may be configured for collectivelyperforming the operations of a same analysis. In the distributedconfiguration, the performance of the plurality of operation units maybe controlled or coordinated by, for example, the server 120.

The acquired information, an intermediate result of the analysis, or aresult of the analysis (for example, a physiological parameter ofinterest) may be analog or digital. In an exemplary context of bloodpressure monitoring, the acquired information, an intermediate result ofthe analysis, or a result of the analysis (for example, a physiologicalparameter of interest) may include, for example, a PPG signal, an ECGsignal, a BCG signal, a BP, a SBP, a DBP, a PR, a HR, a HRV (heart ratevariation), cardiac murmur, blood oxygen saturation, a blood density, apH value of the blood, a bowel sound, a brainwave, a fat content, ablood flow rate, or the like, or a combination thereof.

A result of the analysis, for example, a physiological parameter ofinterest regarding a subject, may be influenced by various factors orconditions including, for example, an environmental factor, a factor dueto a physiological condition of a subject, a factor due to apsychological condition of a subject, or the like, or a combinationthereof. One or more of such factors may influence the accuracy of theacquired information, the accuracy of an intermediate result of theanalysis, the accuracy of a result of the analysis, or the like, or acombination thereof. For instance, a physiological parameter of interestmay be estimated based on a correlation with the acquired information; afactor due to a physiological condition may cause a deviation from thecorrelation; the factor may influence the accuracy of the physiologicalparameter of interest that is estimated based on the correlation. Merelyby way of example, a cardiovascular signal relating to a subject mayvary with, for example, time, the psychological condition of thesubject, the psychological condition of the subject, or the like, or acombination thereof. The correlation between a cardiovascular signalwith a physiological parameter of a subject may vary with, for example,the physiological condition of the subject, the psychological conditionof the subject, the ambient surrounding the subject, or the like, or acombination thereof. Such an influence may be counterbalanced in theanalysis.

In an analysis, information relating to an influencing condition (forexample, environmental information, a physiological condition, apsychological condition, or the like) may be acquired, and a correctionor adjustment may be made accordingly in the analysis. Merely by way ofexample, the correction or adjustment may be by way of a correctionfactor. For instance, an environmental correction factor may beintroduced into the analysis based on acquired environmental informationfrom or relating to the ambient surrounding a subject at or around theacquisition time. Exemplary environmental information may include one ormore of temperature, humidity, air pressure, an air flow rate, anambient light intensity, or the like. Exemplary environmental correctionfactors may include one or more of a temperature correction factor, ahumidity correction factor, an air pressure correction factor, an airflow rate correction factor, an ambient light intensity correctionfactor, or the like. As another example, the correction or adjustmentmay be by way of performing a calibration of the correlation (forexample, a calibrated model, a calibrated function, or the like) used toestimate the physiological parameter of interest. As a further example,the correction or adjustment may be by way of choosing, based oninformation relating to an influencing condition, a correlation from aplurality of correlations used to estimate the physiological parameterof interest.

This description of the analysis module 220 is intended to beillustrative, and not to limit the scope of the present disclosure. Manyalternatives, modifications, and variations will be apparent to thoseskilled in the art. The features, structures, methods, and othercharacteristics of the exemplary embodiments described herein may becombined in various ways to obtain additional and/or alternativeexemplary embodiments. For example, a cache unit (not shown in FIG. 2)may be added to the analysis module 220 used for storing an intermediateresult or real time signal or information during the processes abovementioned.

The output module 230 may be configured for providing an output. Theoutput may include a physiological parameter of interest, at least someof the acquired information (for example, the acquired information thatis used in estimating the physiological parameter of interest), or thelike, or a combination thereof. The transmission of the output may bevia a wired connection, a wireless connection, or the like, or acombination thereof. The output may be transmitted real-time once theoutput is available for transmission. The output may be transmittedafter a delay since the output is available for transmission. The delaymay be in the order of seconds, or minutes, or hours, or days, orlonger. After the delay, the output may be triggered by an instructionfrom a subject, a user other than the subject, or a related third party,an instruction stored in the system 100, or the like, or a combinationthereof. Merely by way of example, the instruction stored in the system100 may specify the duration of the delay, the time the output is to betransmitted, the frequency output is to be transmitted, a triggeringevent, or the like, or a combination thereof. The instruction stored inthe system 100 may be provided by a subject or a user other than thesubject. An exemplary triggering event may be that the physiologicalparameter of interest or that at least some of the acquired informationexceeds a threshold. Merely by way of example, a triggering event may bethat the acquired heart rate exceeds a threshold (for example, higherthan 150 beats per minute, lower than 40 beats per minute, or the like).As another example, a triggering event may be that the physiologicalparameter of interest, for example, an estimated blood pressure, exceedsa threshold.

The output for transmission may be of, for example, an analog form, adigital form, or the like, or a combination thereof. The output may bein the format of, for example, a graph, a code, a voice message, text,video, an audio alert, a haptic effect, or the like, or a combinationthereof. The output may be displayed on a local terminal, or transmittedto a remote terminal, or both. A terminal may include, for example, apersonal computer (PC), a desktop computer, a laptop computer, a smartphone, a smart watch, or the like, or a combination thereof. Merely byway of example, an output may be displayed on a wearable or portabledevice a subject wears, and also transmitted to a computer or terminalat a doctor's office or a hospital where a health care provider islocated and monitors the health condition of the subject.

The output module 230 may include or communicate with a display deviceconfigured to display output or other information to a subject or a userother than the subject. The display device may include a liquid crystaldisplay (LCD), a light emitting diode (LED)-based display, or any otherflat panel display, or may use a cathode ray tube (CRT), a touch screen,or the like. A touch screen may include, for example, a resistance touchscreen, a capacity touch screen, a plasma touch screen, a vectorpressure sensing touch screen, an infrared touch screen, or the like, ora combination thereof.

The peripheral equipment 240 may include any kind of local or remoteapparatuses or devices relating to or communicating with the system 100,or a portion thereof. For example, the peripheral equipment 240 mayinclude a storage device, display equipment, a measuring device, aninput device, or the like, or a combination thereof.

In some embodiments, a storage module (not shown in FIG. 2) or a storageunit (not shown in FIG. 2) may be integrated in the engine 200. In someembodiments, a storage unit (not shown in FIG. 2) may be integrated inany one of the information acquisition module 210, the analysis module220, or the output module 230. The storage module (not shown in FIG. 2)or the storage unit (not shown in FIG. 2) may be used for storing anintermediate result, or a result of an analysis. The storage module (notshown in FIG. 2) or the storage unit (not shown in FIG. 2) may be usedas a data cache. The storage module (not shown in FIG. 2) or the storageunit (not shown in FIG. 2) may include a hard disk, a floppy disk,selectron storage, RAM, DRAM, SRAM bubble memory, thin film memory,magnetic plated wire memory, phase change memory, flash memory, clouddisk, or the like, or a combination thereof. The storage module (notshown in FIG. 2) or the storage unit (not shown in FIG. 2) may includememory or electronic storage media described in connection with FIG. 1and elsewhere in the present disclosure.

In some embodiments, the engine 200 does not include a storage module ora storage unit, and the peripheral equipment 240 or the server 120 maybe used as a storage device accessible by the engine 200. The server 120may be a cloud server providing cloud storage. As used herein, cloudstorage is a model of data storage where digital data are stored inlogical pools, physical storage spanning multiple servers (and oftenlocated at multiple locations). The physical environment including, forexample, the logical pools, the physical storage spanning multipleservers may be owned and managed by a hosting company. The hostingcompany may be responsible for keeping the data available andaccessible, and the physical environment protected and running. Suchcloud storage may be accessed through a cloud service, a web serviceapplication programming interface (API), or by applications that utilizethe API. Exemplary applications include cloud desktop storage, a cloudstorage gateway, a Web-based content management system, or the like, ora combination thereof. The server 120 may include a public cloud, apersonal cloud, or both. For example, the acquired information may bestored in a personal cloud that may be accessed after authorization byway of authenticating, for example, a username, a password, a secretcode, or the like, or a combination thereof. Non personalizedinformation including, for example, methods or calculation models, maybe stored in a public cloud. No authorization or authentication isneeded to access the public cloud. The information acquisition module210, the analysis module 220 and the output module 230 may retrieve orload information or data from the public cloud or the personal clouds.Any one of these modules may be configured to transmit signals and datato the public cloud or personal cloud.

Connection or transmission between any two of the informationacquisition module 210, the analysis module 220, and the output module230 may be via a wired connection, a wireless connection, or the like,or a combination thereof. At least two of these modules may be connectedwith different peripheral equipment. At least two of these modules maybe connected with the same peripheral equipment. The peripheralequipment 240 may be connected with one or more modules via a wiredconnection, a wireless connection, or the like, or a combinationthereof. Those skilled in the art should understand that the aboveembodiments are only utilized to describe the invention in the presentdisclosure. There are many modifications and variations to the presentdisclosure without departing the spirit of the invention disclosed inthe present disclosure. For example, the information acquisition module210 and the output module 230 may be integrated in an independent moduleconfigured for acquiring and outputting signals or results. Theindependent module may be connected with the analysis module 220 via awired connection, a wireless connection, or the like, or a combinationthereof. The three modules in the engine 200 may be partially integratedin one or more independent modules or share one or more units.

The connection or transmission between the modules in the system 100, orbetween the modules and the peripheral equipment 240, or between thesystem and the server 120 should not be limited to the descriptionsabove. All the connections or transmissions may be used in combinationor may be used independently. The modules may be integrated in anindependent module, i.e. functions of the modules may be implemented bythe independent module. Similarly, one or more modules may be integratedon a single piece of peripheral equipment 240. Any one of theconnections or transmissions mentioned above may be via a wiredconnection, a wireless connection, or the like, or a combinationthereof. For example, the wired connection or wireless connection mayinclude, for example, a wire, a cable, satellite, microwave, blue tooth,radio, infrared, or the like, or a combination thereof.

The engine 200 may be implemented on one or more processors. The modulesor units of the engine 200 may be integrated in one or more processors.For example, the information acquisition module 210, the analysis module220, and the output module 230 may be implemented on one or moreprocessors. The one or more processors may transmit signals or data witha storage device (not shown in FIG. 2), the peripheral equipment 240,and the server 120. The one or more processors may retrieve or loadsignals, information, or instructions from the storage device (not shownin FIG. 2), the peripheral equipment 240, or the server 120, and processthe signals, information, data, or instructions, or a combinationthereof, to calculate one or more physiological parameters of interest.The one or more processors may also be connected or communicate withother devices relating to the system 100, and transmit or share signals,information, instructions, the physiological parameters of interest, orthe like with such other devices via, for example, a mobile phone APP, alocal or remote terminal, or the like, or a combination thereof.

FIG. 3 is a flowchart showing an exemplary process for estimating aphysiological parameter of interest according to some embodiments of thepresent disclosure. Information regarding a subject may be acquired instep 310. The information acquisition may be performed by theinformation acquisition module 210. The acquired information may includephysiological information of the subject, environmental informationrelating to the ambient surrounding the subject at or around theacquisition time, information provided by the subject or a user otherthan the subject. The acquired information may include a PPG signal, anECG signal, a pulse rate, a heart rate, a heart rate variation, bloodoxygen saturation, respiration, muscle state, skeleton state, abrainwave, a blood lipid level, a blood sugar level, the height, theweight, the age, gender, the body temperature, the arm length, anillness history, the room temperature, humidity, air pressure, an airflow rate, the ambient light intensity, or the like, or a combinationthereof. At least some of the acquired information may be analyzed at320. Via the analysis, various features of at least some of the acquiredinformation may be identified. For example, the acquired information mayinclude a PPG signal and an ECG signal; the identified features of thesesignals may include, for example, waveform, characteristic points, peakpoints, valley points, amplitude, time intervals, phase, frequencies,cycles, or the like, or a combination thereof. Analysis based on theidentified features may be carried out in step 320. For example, thephysiological parameter of interest may be calculated or estimated basedon the identified features. The physiological parameter of interestestimated based on the acquired PPG signal and ECG signal may include,for example, the BP, the SBP, the DBP, or the like, or a combinationthereof. The physiological parameter of interest may be outputted instep 330. Some of the acquired information may be outputted in step 330.The output may be displayed to the subject or a user other than thesubject, printed, stored in a storage device or the server 120,transmitted to a device further process, or the like, or a combinationthereof. It should be noted that after analysis in step 320, a newacquisition step may be performed in step 310.

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. For example, apre-treatment step may be added between step 310 and step 320. In thepre-treatment step, the acquired signals may be pre-treated, in order toreduce or remove noise or interferences in the signals originallyacquired. For example, a sophisticated, real-time digital filtering maybe used to reduce or remove high-frequency noise from the PPG or ECGsignal, allowing their features to be accurately identified. Exemplarypre-treatment methods may include low-pass filtering, band-passfiltering, wavelet transform, median filtering, morphological filtering,curve fitting, Hilbert-Huang transform, or the like, or a combinationthereof. Descriptions regarding methods and systems for reducing orremoving noise from a physiological signal, for example, a PPG signal oran ECG signal, may be found in, for example, International PatentApplication Nos. PCT/CN2015/077026 filed Apr. 20, 2015,PCT/CN2015/077025 filed Apr. 20, 2015, and PCT/CN2015/079956 filed May27, 2015, each of which is incorporated by reference. One or more otheroptional steps may be added between step 310 and step 320, or elsewherein the exemplary process illustrated in FIG. 3. Examples of such stepsmay include storing or caching the acquired information.

FIG. 4 is a block diagram illustrating an architecture of an informationacquisition module according to some embodiments of the presentdisclosure. The information acquisition module 210 may be connected toor otherwise communicate with, for example, the peripheral equipment240, other modules 490, and the server 120 through the network 150. Theinformation acquisition module 210 may be configured to acquirephysiological signals, basic information of a subject, environmentalinformation surrounding the subject, or the like, or a combinationthereof. The information acquisition module 210 may include one or moreacquisition units (such as a basic information acquisition unit 410, anenvironmental information acquisition unit 420, an ECG acquisition unit430, or the like, as shown in FIG. 4), a control unit 470, and a storageunit 480. The one or more acquisition units may be configured foracquiring information relating to a subject, information provided by thesubject, a user other than the subject, and/or a related third party(for example, a doctor, a healthcare worker, a medical institution, aresearch facility, a peripheral device of the subject or a userwell-connected to the subject, or the like), environmental informationfrom the ambient surrounding the subject at or around the acquisitiontime, or the like, or a combination thereof. The one or more acquisitionunits may include a basic information acquisition unit 410, anenvironmental information acquisition unit 420, an ECG acquisition unit430, a PPG acquisition unit 440, an electromyography (EMG) acquisitionunit 450, a blood oxygen acquisition unit 460, or the like, or acombination thereof. In some embodiments, the one or more acquisitionunits may be connected or otherwise communicate with the control unit470 and the storage unit 480 in real time. The acquisition process maybe controlled in real time, and the storage of the acquired informationmay be performed in real time. In some embodiments, the one or moreacquisition units may be connected or otherwise communicate with thecontrol unit 470 and the storage unit 480 after a time delay. Theacquisition process may be controlled or modified after a time delaysince an acquisition cycle is finished. The storage of the acquiredinformation may be performed after a time delay since the information isacquired. As used in herein, the time delay may be in the order ofseconds, or minutes, or hours, or days, or longer, or the like.Similarly the one or more acquisition units may be connected orotherwise communicate with the peripheral equipment 240, or the server120, or other modules 490 in real time or after a time delay.

The basic information acquisition unit 410 may be configured forreceiving the basic information relating to the subject including, forexample, the height, the weight, the age (or the date of birth), thegender, the arm length, the nationality, the occupation, a habit (forexample, a health-related habit such as an exercise habit), theeducation background, a hobby, the marital status, religious belief, ahealth-related history (for example, whether a subject has a history ofsmoking, a food allergy, a drug allergy, a medical treatment history, afamily health history, a history of genetic disease, informationregarding a prior surgery, or the like, or a combination thereof),contact information, emergency contact, or the like, or a combinationthereof. The basic information relating to the subject may be providedby the subject, a user other than the subject, or a third party (forexample, a doctor, a healthcare worker, a medical institution, aresearch facility, a peripheral device of the subject or a userwell-connected to the subject, or the like). The basic informationrelating to the subject may be loaded from the peripheral equipment 240or the server 120. The basic information relating to the subject may bestored in the storage unit 480, or may be stored in the server 120, ormay be stored in the peripheral equipment 240, or may be stored in astorage device disclosed anywhere in the present disclosure.

The environmental information acquisition unit 420 may be configured foracquiring environmental information surrounding the subject, includingtemperature, humidity, air pressure, an air flow rate, an ambient lightintensity, or the like, or a combination thereof. The environmentalinformation may be acquired in a real time mode (for example, at oraround the acquisition time), or may be acquired at a certain timeinterval (for example, independent of the acquisition time). Theenvironmental information may be loaded from the server 120, or may beloaded from some environment relating APPs (for example, a weatherforecast APP). The acquired environmental information may be stored inthe storage unit 480, or may be stored in the server 120, or may bestored in the peripheral equipment 240, or may be stored in a storagedevice disclosed anywhere in the present disclosure.

The ECG acquisition unit 430 may be configured for acquiring thesubject's ECG signals by way of an electrode sensing method. Theelectrode sensing method may be a 12-lead method. The electrode sensingmethod may be any conventional electrocardiographic lead method. Theacquired ECG signals may be stored in the storage unit 480, or may bestored in the server 120, or may be stored in peripheral equipment 240,or may be stored in a storage devices disclosed anywhere in the presentdisclosure. The acquired signals may be transmitted to other modules 490(for example, the analysis module 220 or the output module 230) in realtime or after a time delay. It should be noted that the ECG acquisitionunit 430 may be configured for receiving the subject's ECG signals froma related peripheral device. The related peripheral device may be an ECGmonitor, a household ECG monitor, a portable ECG monitor, a medical ECGmonitor, or the like, or a combination thereof.

The PPG acquisition unit 440 may be configured for acquiring thesubject's PPG signals by way of a photoelectric sensing method. Thephotoelectric sensing method may be implemented by a singlephotoelectric sensor, or may be implemented by an array of photoelectricsensors, or may be implemented by a light source and an array ofreceiving ends. The acquired PPG signals may be stored in the storageunit 480, or may be stored in the server 120, or may be stored inperipheral equipment 240, or may be stored in a storage devicesdisclosed anywhere in the present disclosure. The acquired PPG signalsmay be transmitted to other modules 490 (for example, the analysismodule 220 or the output module 230) in real time or after a time delay.The PPG acquisition unit 440 may be configured for acquiring thesubject's PPG signals from multiple body locations (for example, thehead, the neck, the chest, the abdomen, the upper arm, the wrist, thewaist, the upper leg, the knee, the ankle, or the like, or a combinationthereof). In some embodiments, one or more photoelectric sensors may beplaced on any one of the multiple body locations. In some embodiments,one or more photoelectric sensor arrays may be placed on any of themultiple body locations. It should be noted that the PPG acquisitionunit 440 may be configured for receiving the subject's PPG signals froma related peripheral device. The related peripheral device may be a PPGmonitor, a household PPG monitor, a portable PPG monitor, a medical PPGmonitor, or the like, or a combination thereof.

The EMG acquisition unit 450 may be configured for acquiring thesubject's EMG signals by way of a pressure sensing method. An electrodepatch may be placed on the surface of the body to record a potentialchange, or an electrode needle may be stick into the body surface torecord a local potential change. The blood oxygen acquisition unit 460may be configured for acquiring the subject's blood oxygen informationby way of a photoelectric sensing method. The blood oxygen informationmay be acquired together with an acquisition of a PPG signal, or may beacquired independently. The acquired EMG signals and blood oxygeninformation may be stored in the storage unit 480, or may be stored inthe server 120, or may be stored in the peripheral equipment 240, or anystorage device disclosed anywhere in the present disclosure.

The one or more acquisition units may communicate with one or moresensors to acquire information sensed, detected or measured by the oneor more sensors. Exemplary sensors include an electrode sensor, anoptical sensor, a photoelectric sensor, a conductance sensor, a pressuresensor, an accelerometer, a gravity sensor, a temperature sensor, amoisture sensor, or the like, or a combination thereof.

Merely by way of example, an optical sensor may include an integratedphotodetector, amplifier, and a light source. The light source may emitradiation of wavelengths of, for example, the visible spectrum, theinfrared region, or the like, or a combination thereof. Thephotodetector may detect the reflected radiation. The optical sensor maybe placed at a body location on a subject to detect a pulse-relatedsignal of a subject. In one example, multiple wearable PPG sensors maybe placed at multiple body locations on a subject. The multiplelocations may include the head, the neck, the chest, the abdomen, theupper arm, the wrist, the waist, the upper leg, the knee, the ankle, orthe like, or a combination thereof. In another example, a PPG sensorarray including a series of PPG sensors may be placed at a body locationon a subject. In some embodiments, the PPG sensors may be assembled intoone device. The device may be a wearable or portable device including,for example, a T-shirt, a smart watch, a wristband, or the like, or acombination thereof. The device may further include one or moreprocessors or processing units. The processor or the processing unit maybe configured for controlling the process of information acquisition, ormay be configured for performing one or more operations of any of themodules. Signals or data may be transmitted between sensors placed atdifferent locations. The transmission may be via a wireless connection(for example, wifi, blue tooth, near-field communication (NFC), or thelike, or a combination thereof), a wired connection, or the like, or acombination thereof. For example, signals received by the sensors may betransmitted through a wireless body sensor network (BSN) or anintra-body communication (IBC).

The information acquisition module 210 may include other one or moreacquisition units other than those described above, such as anacquisition unit (not shown in FIG. 4) configured for acquiring a BCGsignal, an acquisition unit (not shown in FIG. 4) configured foracquiring body temperature information of the subject, an acquisitionunit (not shown in FIG. 4) configured for acquiring a blood densityinformation of the subject, an acquisition unit (not shown in FIG. 4)configured for acquiring a pH value information of the blood, or thelike, or a combination thereof.

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. For example, theacquisition units may be integrated into an independent unit configuredfor acquiring more than one information or signal relating to thesubject. At least some of the acquisition units may be integrated intoone or more independent units.

The control unit 470 may be configured for controlling a factor or acondition during the information acquisition process. The factor orcondition may include ON/OFF of the acquisition units, the configurationof information acquisition, a sampling rate, an acquisition cycle, thetransformation or processing of the acquired information, the sequenceof acquiring different signals, the selection of one or more sensors orsensor arrays, the arrangement of the sensors, the arrangement of asensor array, the number and/or shape of one or more electrodes of asensor array, the selection of one or more light sources, thesensitivity or accuracy of a sensor or sensor array, or the like, or acombination thereof.

Merely by way of example, the control unit 470 may be configured forcontrolling the selection of different PPG sensors or PPG sensor arraysfor a signal acquisition process according to some embodiments of thepresent disclosure. As described above, the sensor configuration in thePPG acquisition unit 440 may be a single photoelectric sensor, or aplurality of photoelectric sensors, or a single sensor array, or aplurality of sensor arrays. A specific sensor configuration may beselected during a specific acquisition process. The selection of asensor configuration may be performed based on a feature of a subject.Exemplary features may include height, weight, gender, body size, or thelike, or a combination thereof. In some embodiments, the selection maybe performed through a stepper machine, or through a micro-motor. Insome embodiments, the selection may be performed using memory materials(for example, a memory metal). In another example, the control unit 470may be configured for adjusting a location of a sensor or a sensor arrayaccording to the body size of a subject. In another example, the controlunit 470 may be configured for adjusting the arrangement of a sensor ora sensor array according to the height or weight of a subject. Theprocess of the adjustment may be performed automatically, or may beperformed at least partially manually. For instance, the control unit470 may calculate or determine, based on the height of a subject, adesirable location of a sensor; the subject, a user other than thesubject, and/or a related third party may manually place the sensor orsensor array at the desirable location.

The control unit 470 may include one or more control sub-units (notshown in FIG. 4). In some embodiments, the control sub-units (not shownin FIG. 4) may perform different control functions, respectively. Insome embodiments, the control sub-units (not shown in FIG. 4) may beconfigured for controlling different acquisition units, respectively. Insome embodiments, the control sub-units (not shown in FIG. 4) mayperform one or more control steps and control the informationacquisition in series (for example, a control step performed afteranother control step at or around the same time). The control unit 470may include a micro-processor. The micro-processor may be configured toexecute one or more instructions provided by the subject, a user otherthan the subject, and/or a related third party.

The storage unit 480 may be configured for storing acquired information,control factor or condition, or inter-data during the informationacquisition. The storage unit 480 may include one or both of a systemstorage (for example, a disk) that is provided integrally (i.e.substantially non-removable) with the component, and a removable storagethat is removably connectable to the component via, for example, a port(for example, a USB port, a firewire port, etc.) or a drive (forexample, a disk drive, etc.). The storage unit 480 may include or beconnectively operational with one or more virtual storage resources (forexample, cloud storage, a virtual private network, and/or other virtualstorage resources). The storage unit 480 may include a hard disk, afloppy disk, selectron storage, RAM, DRAM, SRAM bubble memory, thin filmmemory, magnetic plated wire memory, phase change memory, flash memory,cloud disk, or the like, or a combination thereof. The storage unit 480may be connected or otherwise communicate with other modules 490, theserver 120, and the peripheral equipment 240. The storage unit 480 mayinclude memory or electronic storage media described in connection withFIG. 1 and elsewhere in the present disclosure.

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. For example, thestorage unit 480 is not necessary, the storage may be implementedthrough other storage media described elsewhere in the presentdisclosure. The storage unit 480 may be integrated into the acquisitionunits or may be integrated in the control unit 470.

FIG. 5-A and FIG. 5-B are schematic diagrams showing an exemplaryarrangement or location of the ECG sensors and PPG sensors according tosome embodiments of the present disclosure. An ECG sensor may be anelectrode sensor including one or more electrodes. The one or moreelectrodes may be used for sensing the potential change indicating thecardiac activity of a subject wearing the sensor. Merely by way ofexample, an ECG sensor may include 10 electrodes in a 12-lead form foracquiring ECG signals. FIG. 5-A illustrates exemplary locations on thebody of a subject where the 10 electrodes of the ECG sensor may beplaced according to a 12-lead form. In the 12-lead form, the 10electrodes may be located on one or more limbs and/or on the he chest ofthe subject, as shown in FIG. 5-A. For instance, the locations of the 10electrodes may include the right arm (RA), the left arm (LA), the rightleg (RL), the left leg (LL), the fourth intercostal space to the rightof the sternum (V1), the fourth intercostal space to the left of thesternum (V2), the fifth intercostal space in the mid-clavicular line(V4), between leads V2 and V4 (V3), horizontally even with V4 in theleft anterior axillary line (V5), and horizontally even with V4 and V5in the mid axillary line (V6). The 10 electrodes may form 12 differentleads representing or measuring the electrical potential differencebetween pairs of body points. In this embodiment, the overall magnitudeof the heart's electrical potential may be measured from 12 differentangles (“leads”). It is understood that the 12-lead ECG sensor isdescribed for illustration purposes, and not intended for limiting thescope of the present disclosure. An ECG sensor of another form may beused for acquired ECG signals, e.g., a five-lead form. Also see, forexample, International Application No. PCT/CN2015/083334 filed Jul. 3,2015, the entire contents of which are hereby incorporated by reference.

According to some embodiments of the present disclosure, the PPG sensormay be a photoelectric sensor. FIG. 5-B illustrates the locations on thebody of a subject where the photoelectric sensors may be placed. Thephotoelectric sensor may include an emitting end for emitting a lightsource and a receiving end used for acquiring a signal resulting fromthe emitted light source. The acquired signal may be used to derive orprovided a PPG value. For brevity, a PPG signal, as used herein, mayrefer to the derived PPG value, or the acquired signal used to derivethe PPG value. The light source may include a light source of a suitablewavelength including, for example, red, green, blue, infrared, purple,yellow, orange, or the like, or a combination thereof. The spectrum ofthe light sources may include visible spectrum, infrared spectrum,far-infrared spectrum, or the like, or a combination thereof. Thereceiving end may be a detector that may detect the quantity of thereceived signals and/or a change thereof, and/or provide a correspondingoutput (for example, an electrical signal or an optical signal).

As shown in FIG. 5-B, the photoelectric sensors may be placed on or atmultiple body locations of a subject. The locations may include thehead, the neck, the chest, the abdomen, the upper arm, the wrist, thewaist, the upper leg, the knee, the ankle, or the like, or a combinationthereof. The photoelectric sensors placed on or at multiple bodylocations may be used for acquiring multiple PPG signals including thepulse related information of the corresponding body locations. Merely byway of example, the light source of the photoelectric sensor placed onthe head of the subject may emit one or more light signals to the nearbyblood vessels and the receiving end may acquire one or more signalsresulting from the emitted light source. In some embodiments, theacquired signal may be or relate to a PPG signal indicating a pulse orblood related information of the head or a portion thereof. In someembodiments, the acquired PPG signal may be used for calculating aphysiological parameter of interest (for example, blood pressure)indicating a health condition of the head, or a portion thereof, of thesubject. In some embodiments, a variation of the PPG wave may indicate avariation of the blood vessel(s) or a variation of the blood viscosityof the nearby location of the head, or a portion thereof, of thesubject. Similarly, blood related information of other body locationsmay be acquired by one or more photoelectric sensors placed on one ormore body locations.

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. For example, inaddition to the location configuration shown in FIG. 5-B, one or morephotoelectric sensors may be added to other body locations. On a bodylocation, a sensor array other than an independent sensor may be placed(will be described in detail in FIG. 6).

FIG. 6-A through FIG. 6-D are schematic diagrams showing exemplarysensor arrays according to some embodiments of the present disclosure. Asensor array may include a plurality of portions. A portion of thesensor array may be a sensor, or a receiving end of a sensor, or anemitting end of a sensor. As shown in FIG. 6-A through FIG. 6-D, theportions of the sensor array may be arranged in an oval array, arectangular array, a rhombus array, a circular array, or the like, or acombination thereof.

In some embodiments, a portion of the sensor array may be a sensor. Thesensor array may include one or more types of sensors. For instance, asensor array may include at least one photoelectric sensor and at leastanother type of sensor. In one example, the sensor array may include aseries of PPG sensors. The sensor array may be configured for acquiringa series of PPG signals.

In some embodiments, a portion of the sensor array may be a receivingend of a sensor. The sensor array may include one or more receivingends. The one or more receiving ends may be configured for receivingsignals based on a same light source, or based on different lightsources. The light sources may be emitted by one or more emitting end ofa sensor. The one or more emitting ends may be integrated in the sensorarray, or located outside of the sensor array, or located in any moduleor unit of the system. In one example, during the detection of PPGsignals, the one or more emitting ends may emit a series of lightsignals simultaneously or successively toward a location of interest.Thus the sensor array may acquire a series of signals. In someembodiments, the series of acquired signals may be used to provide aseries of PPG signals detected from the body location of interest.

In some embodiments, a portion of the sensor array may be an emittingend of a sensor. The sensor array may include one or more emitting ends.The one or more emitting ends may be configured for emitting a series oflights toward a body location of interest. One or more receiving endsmay be integrated in the sensor array, or located outside of the sensorarray, or located in a module or a unit nearby the sensor array. The oneor more receiving ends may acquire a series of signals based on theseries of lights emitted by the series of emitting ends. In someembodiments, the series of the acquired signals may be used to provide aseries of PPG signals detected from the body location of interest.

In some embodiments, the signals acquired by a sensor array may beanalyzed and/or processed using one or more signal processing methodsincluding, for example, those described in FIG. 8-A through FIG. 8-E. Insome embodiments, the acquired signals may be processed or analyzed togenerate a new signal. Merely by way of example, the new signal may bethe mean value or the average value of the acquired signals. As anotherexample, the signals acquired by a sensor array may include a firstacquired signal and a second acquired signal; the first acquired signalmay indicate a measurement error; the second acquired signal may includeor be affected by the measurement error; the first acquired signal maybe used to correct or otherwise modify a second acquired signal. Forinstance, a series of PPG signals may be acquired using a sensor array.The sensor array may include an emitting end of a PPG sensor that mayemit different light signals, and two receiving ends that may acquirePPG signals relating to PPG based on the emitted light signals. A firstacquired signal may relate to a measurement error arising from themovement of an internal organ of a subject; a second acquired signal maybe a coarse PPG signal including at least part of the measurement error;the first acquired signal and the second acquired signal may be analyzedand processed to generate a new PPG signal that at least part of themeasurement error is corrected. The new PPG signal may be used forcalculating a physiological parameter of interest in subsequent steps.

FIG. 6-A illustrates an oval array including eleven portions. FIG. 6-Billustrates a rectangular array including nine portions. FIG. 6-Cillustrates a rhombus array including nine portions. FIG. 6-Dillustrates a circular array including nine portions.

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. For example, otherthan the oval array, the rectangular array, the rhombus array, thecircular array, other types of array may be used for the sensor arraydesign. As another example, an oval array may include more than or fewerthan eleven portions. As still another example, the locations of theportions may be different from those illustrated in FIG. 6-A throughFIG. 6-D. For instance, a rectangular array may be a 3×3 array, a 6×6array, a 9×9 array, an m×m array, an m×n array in which m may bedifferent from n, or the like, or a combination thereof. In still afurther example, a sensor array may include at least two types ofsensors. For instance, a sensor array may include a PPG sensor and atemperature sensor.

FIG. 7 is a flowchart of an exemplary process for estimating a bloodpressure according to some embodiments of the present disclosure.Beginning in step 710, information including a first signal and one ormore a plurality of second signals may be acquired. In some embodiments,a plurality of first signals may be acquired. The acquisition of thesignals or related information may be performed by the informationacquisition module 210. At least some of the signals or relatedinformation may be acquired by one or more sensors. At least some of thesensors may be part of or communicate with the system 100 or a portionthereof. At least some of the signals or related information may beprovided by, for example, a subject, a user other than the subject, or athird party. At least some of the signals or related information may beretrieved from the information acquisition module 210, the server 120,the storage unit 480, a storage device anywhere disclosed in the presentdisclosure, or the like, or a combination thereof. Merely by way ofexample, the first and the second signals may be physiological signalsincluding, for example, an electrocardiogram (ECG) signal, apulse-related (or pulse-wave-related) signal (for example, a PPGsignal), a phonocardiogram (PCG) signal, an impedance cardiogram (ICG)signal, or the like, or any combination thereof. For example, the firstsignal may be an ECG signal; the second signal(s) may be a plurality ofPPG signals. The ECG signal may be detected in a 12-lead formillustrated in FIG. 5-A. The plurality of second signals may be acquiredby a plurality of sensors or sensor arrays. For instance, a plurality ofsecond signals may be acquired using a plurality of sensors, a pluralityof sensor arrays placed on multiple body locations of the subjectillustrated in FIG. 5-B. As another example, at least one second signalmay be acquired using a sensor; at least one second signal may beacquired using a sensor array; the sensor and the sensor array may beplaced at a same body location of the subject, or at different bodylocations of the subject.

In some embodiments, the first signal and the plurality of secondsignals may be acquired simultaneously, at or around the same time. Insome embodiments, the plurality of second signals may be acquiredsimultaneously, at or around the same time. In some embodiments, theplurality of second signals may be acquired successively.

In step 720, at least some of the acquired information may bepre-treated. In some embodiments, the acquired first signal and/or theacquired plurality of second signals may be pre-treated. Thepre-treatment may be performed to reduce or remove noise orinterferences in the signals or signal related data. The pre-treatmentmay be performed by the analysis module 220. Exemplary methods that maybe used in the pre-treatment may include low-pass filtering, band-passfiltering, wavelet transform, median filtering, morphological filtering,curve fitting, Hilbert-Huang transform, or the like, or any combinationthereof. During the process of the pre-treatment, the methods mentionedherein may be used in parallel or may be used in combination.Descriptions regarding methods and systems for reducing or removingnoise from a physiological signal, for example, a PPG signal or an ECGsignal, may be found in, for example, International Patent ApplicationNos. PCT/CN2015/077026 filed Apr. 20, 2015, PCT/CN2015/077025 filed Apr.20, 2015, and PCT/CN2015/079956 filed May 27, 2015, each of which isincorporated by reference. Additionally, real-time transformation oftime domain or frequency domain may also be implemented in step 720, andthe signals or related information may be used in time domain, frequencydomain, or both.

In step 730 and in step 740, a first feature of the first signal and aplurality of second features of the plurality of second signals may berecognized. The recognition of the first feature and the second featuresmay be performed simultaneously, at or around the same time, or may beperformed successively. In the exemplary context of blood pressuremonitoring, the first signal or the second signals may include an ECGsignal, a PPG signal, a BCG signal, or the like; exemplary features ofthe first signal or the second signals may include waveform,characteristic points, amplitude, phase, frequency, cycle, a first-ordermoment of a signal, a high-order moment of a signal, a first-orderderivative of a signal, a high-order derivative of a signal, or thelike, or any combination thereof. The characteristic points may includea peak point, a valley point, a fastest rising point of a wave, astarting point of a wave, an end point of a wave, or the like, or acombination thereof. In some embodiments, the features recognized instep 730 and in step 740 may include a peak point of the R wave on anECG signal, a maximum positive peak of a PPG signal, a fastest risingpoint of a PPG signal, a start point of a PPG wave, or the like, or acombination thereof.

In some embodiments, before the recognition of the plurality of secondfeatures, the plurality of second signals may be analyzed and processed.In some embodiments, the plurality of second signals may bemathematically processed. One second signal from the plurality of secondsignals may be used for correcting one or more second signals of theplurality of second signals. The plurality of second signals may beanalyzed and processed to generate, one or more new signals (also may bereferred to as “a third signal”). In some embodiments, the plurality ofsecond signals may be a plurality of PPG signals acquired by a pluralityof sensors and/or sensor arrays placed on multiple body locations of thesubject. For example, the plurality of second signals may be multiplePPG signals acquired by multiple PPG sensors located on multiple bodylocations of the subject. In another example, the plurality of secondsignals may be multiple series of PPG signals acquired by multiplesensor arrays as illustrated in FIG. 6-A through FIG. 6-D placed on orat multiple body locations of the subject. As used herein, “a series ofPPG signals” refers to PPG signals acquired by a sensor array from abody location of the subject. Thus “multiple series of PPG signals” mayrefer to PPG signals acquired by multiple sensor arrays from multiplebody locations of the subject. Exemplary body locations may include thehead, the neck, the chest, the abdomen, the upper arm, the wrist, thewaist, the upper leg, the knee and the ankle. In some embodiments, aseries of PPG signals acquired by a sensor array may be analyzed andprocessed using one or more signal processing methods including, forexample, those described in FIG. 8-A through FIG. 8-E, to generate a newPPG signal. Thus a plurality of new PPG signals may be generated frommultiple series of PPG signals. In some embodiments, the new PPG signalmay be the mean value or the average value of the plurality of acquiredPPG signals. Then a plurality of second features may be recognized fromthe plurality of new PPG signals in step 740.

In step 750, a plurality of parameters based on the recognized featuresof the first and the plurality of second signals may be determined. Insome embodiments, the parameter may be the pulse transit time (PTT). Asused herein, the pulse transit time may refer to the time intervalbetween the characteristic points of an ECG signal and a pulse waverelated signal including, for example, a PPG signal. In one example, PTTis determined by a time interval between an ECG fiducial point(typically the R peak, but also the Q/S peak, or even the peak of a P/Twave may be used) and a fiducial point marking the pulse arrival (e.g.,a start point of the PPG wave, a fastest rising point, or the like). Inanother example, PTT is determined by a time interval between two pulsewave signals (e.g., two PPG signals) detected at different bodylocations, e.g., the carotid and femoral arteries. The plurality ofdetermined parameters may be used for calculating blood pressures instep 760. In some embodiments, blood pressure may be calculated based onthe determined PTTs in step 760.

A pre-treatment step may be performed to assess an acquired signal (forexample, an ECG signal, a PPG signal, etc.) before one or more featuresof the signal is identified. For instance, an acquired ECG signal may beaccessed before one or more features of the signal is identified. Theassessment may be performed to evaluate whether a valid ECG signal isacquired. The assessment may be performed by way of, for example, apattern recognition process. For instance, the R peak of an acquired ECGsignal may be determined by the pattern recognition process. In someembodiments, the system may identify an abnormal signal or waveform(e.g., an abnormal sinus rhythm R wave, another physiological signal, orthe like) that may be unsuitable for determining PTT; such an abnormalsignal or waveform may be abandoned to avoid to be involved in thesubsequent calculation or analysis. In some embodiments, the acquiredECG signal may be compared with a reference signal to determine whetherthe acquired ECG signal includes an abnormal R wave. The referencesignal may be a normal sinus rhythm ECG signal, or may be retrieved froma database having historical data.

The ECG waveform and the PPG waveform are cyclical signals, i.e. thecharacteristic points occur substantially cyclically or periodically.Thus PTT′ is approximated by a time interval of the maximum point on theQRS complex on the ECG waveform and a peak point on a subsequent(second) PPG waveform. Similarly, PTT″ also may be approximated by atime interval between the peak point on the QRS complex on the ECGwaveform and a peak point on a further (third) PPG waveform. The valueof PTT′ and the value of PTT″ are larger than that of PTT, and errors ordeviations may occur while estimating blood pressure or otherphysiological parameters of interest based on such inaccurate PTT′ andPTT″ values. Such errors or deviations may be avoided or reduced byusing a PPG waveform from the same cycle (driven by the same heart beat)as the ECG waveform. Thus, during recognition of characteristic pointsof the PPG waveform, a threshold may be set regarding the time window orsegment within which the characteristic points on the PPG waveform maybe identified and used to determine PTT. In one example, the time windowmay be 2 seconds or less. Merely by way of example, an analysis toidentify a fiduciary point on a PPG waveform is performed on a segmentof the PPG waveform occurring within 2 seconds from the time point whenthe maximum point on the ECG waveform is identified, in order toapproximate the PTT. As another example, an analysis to identify afiduciary point on a PPG waveform is performed on a segment of the PPGwaveform occurring between two consecutive peak points on the ECGwaveform, in order to approximate the PTT. As a further example, thetime window may be set based on the heart rate of the subject. Forinstance, the time window may be set based on the heart rate of thesubject at or around the acquisition time, or an average heart rate ofthe subject for a period of time, or an average heart rate of a group ofpeople (for example, a sub-group of people who share a same or similarcharacteristic with the subject; exemplary characteristic may includeage, gender, nation, stature, weight, a body fat percentage, color ofskin, a family health history, a life style, an exercise habit or otherhabit, diet, occupation, illness history, education background, maritalstatus, religious belief, or the like, or any combination thereof.

The cycle of ECG or the cycle of PPG may vary. As an example, the cycleof ECG or the cycle of PPG of different subjects may be different. Asanother example, the cycle of ECG or PPG of the same subject may varyunder different situations (e.g., when the subject is exercising orasleep, at different times of a day, at the same or similar time ondifferent days), or the like, or a combination thereof. In one example,the time window threshold may be set based on the heart rate of asubject (for example, the cycle of average person is approximately60-120 beats per minute). The heart rate may be an average value over aperiod of time (e.g., a week, a month, a year, or the like). The heartrate may be one measured at or around the acquisition time. The heartrate may be measured based on, e.g., the ECG signal, the PPG signal, orthe like. The time window may be set or updated based on the measuredheart rate. In another example, the time window may be set by, e.g., thesystem, the subject, or a user other than the subject, based on thephysiological information of the subject. For example, the physiologicalinformation may include motion or not, taking medicine or not, good orbad mood, emotional stress or not, or the like, or a combinationthereof. In another example, the time window may be a fixed valuedefined by the system, the subject, or a user other than the subject(e.g., his doctor, health care provider, or the like).

In step 750, a parameter such as PTTV (pulse transit time variation) maybe approximated based on a group of determined PTT. A parameter such asHRV may be determined based a group of ΔRR. As used herein, ΔRR refersto as a time interval between two adjacent R waves (the maximum point ofa QRS waveform). More descriptions regarding the determination of thePTT may be found in International Patent Application No.PCT/CN2015/083334 filed Jul. 3, 2015, which is hereby incorporated byreference.

In some embodiments, relation information among the plurality ofparameters may be generated (not shown in FIG. 7). The relationinformation may be stored in the server 120, the analysis module 220, orany storage device disclosed anywhere in the present disclosure. In someembodiment, the relation information may provide guidance regarding acompensation term. The compensation term may be added to a model used tocalculate a blood pressure in step 760. See, for example, Equation 9. Insome embodiments, a relation information among the acquired plurality ofsecond signals may be generated (not shown in FIG. 7). Similarly therelation information may be retrieved to generate a compensation term.

In step 760, a plurality of blood pressure values may be calculatedbased on the parameter(s) including, for example, the determined PTT,PTTV, HRV, or the like, or a combination thereof. One or morecalculation models may be stored in the server 120, the analysis module220, or any storage device disclosed anywhere in the present disclosure.During the calculation, one or more favorite models may be retrievedbased on the subject's personal data, universal data, additionalinformation in a history, or the like, or a combination thereof. As usedherein, a favorite model may refer to a model that may provide a moreaccurate estimation of a physiological parameter of interest fromacquired information than one or more other models.

In some embodiments, the plurality of second signals acquired in step710 may be a plurality of PPG signals acquired from multiple bodylocations of the subject. Thus in step 760, a plurality of bloodpressure values of a plurality of body locations may be calculated basedon the plurality of PPG signals. Similarly, in some embodiments, aplurality of physiological parameters of interest of multiple bodylocations may be calculated. Relation information among the plurality ofblood pressures may be generated (not shown in FIG. 7). As describedabove, in some embodiments, relation information among the determinedplurality of parameters or relation information among the plurality ofsecond signals may be generated.

In some embodiments, the relation information may be used to detectwhether an error occur during calculating the physiological parameters.For instance, an error of the second signal at one body location mayindicate a possible problem with the measurement of at least part of orthe entire set of the second signals. As another example, the relationinformation including the distribution of the second signals at multiplebody locations may indicate a pathological condition. For instance, thedeviation from a normal distribution of blood pressure derived from thesecond signals at multiple body locations of a subject may indicatearteriosclerosis.

In some embodiments, a guide information may be generated based on therelation information. The guide information may relate to the selectionof calculation models that may be used to calculate the physiologicalparameter of interest of the subject based on the acquired signalsincluding, for example, the first signal, the second signals, etc. Forinstance, for a subject with arteriosclerosis and a subject withoutarteriosclerosis, different calculation models may be used to calculateblood pressure based on PTT values. The guide information may be amodified model used for calculating the physiological parameter ofinterest of the subject. In some embodiments, the modification may be toinclude a compensation term. See, for example, Equation 9 describedbelow. The compensation term may relate to the distribution orinterrelation of multiple second signals at different body locations ofthe subject, or information derived from the multiple second signals.For instance, the compensation term may relate to the distribution ofPTT values at multiple body locations. The compensation term may be arelationship of the difference between the PTT values at two or morebody locations. The relationship may be expressed in the form of alinear function, an nth degree polynomial, an exponential function, alogarithmic function, a trigonometric function, an anti-trigonometricfunction, a hyperbolic function, or the like, or a combination thereof.For instance, a PTT value at the left arm may be compared to a PTT valueat the right arm. The interrelation or difference between the two PTTvalues may be analyzed to generate a compensation term. As anotherexample, a comparison may be performed among a PPT value at the leftupper arm, a PPT value at the right upper arm, a PPT value at the leftankle, and a PPT value at the right ankle; the interrelation ordifference between the four PTT values may be analyzed to generate acompensation term.

In some embodiments, the compensation term may relate to one or morePTTV values. For instance, one or more PTTV values may be determinedbased on a group of determined PTT values at a body location. Thecompensation term may be the difference between the PTTV values. Therelationship may be expressed in the form of a linear function, an nthdegree polynomial, an exponential function, a logarithmic function, atrigonometric function, an anti-trigonometric function, a hyperbolicfunction, or the like, or a combination thereof. In some embodiments,the compensation term may relate to one or more HRV values (as describedabove). Similarly the compensation term may be a relationship of thedifference between the HRV values.

In some embodiments, the compensation term may relate to a combinationof a PTTV value, an HRV value, the interrelationship or differencebetween two or more PTT values of various body locations, or the like.In some embodiments, the compensation term may be a relationship of aPTTV value and an HRV value. In some embodiments, the compensation termmay be a relationship of an HRV value and the interrelationship ordifference between two or more PTT values of various body locations. Insome embodiments, the compensation term may be a relationship of a PTTVvalue at a body location and the interrelationship or difference betweentwo or more PTT values of various body locations. The relationship maybe expressed in the form of a linear function, an nth degree polynomial,an exponential function, a logarithmic function, a trigonometricfunction, an anti-trigonometric function, a hyperbolic function, or thelike, or a combination thereof.

During the process, a calibration may be performed in step 770. Thecalibration may be performed periodically, upon a subject's instruction,or the like, or a combination thereof. The calibration may taketime-varying properties into account. The time-varying properties mayinclude, e.g., the arterial propagation path of a specific subject, theheart activity of a specific subject, the real-time temperature orhumidity, the updated fiducial BP of a specific subject, the updateddatabase storing historical data (SBP/DBP values, BP calculatingalgorithms, etc.) of a specific subject, the updated database storingreference data of people sharing the same or similar characteristics(e.g., age, gender, stature, weight, a body fat percentage, color ofskin, a family health history, a life style, an exercise habit, diet, apsychological condition, a health condition, an education history,occupation, or the like, or any combination thereof), or the like, orany combination thereof.

The calibration data may include physiological parameters, information(e.g., environmental or personal information) relating to thephysiological parameter, or the like, or a combination thereof.Exemplary physiological parameters may include PTT0, SBP0, DBP0, PTTV0,HRV0, or the like, or a combination thereof. Exemplary models mayinclude different functions or a same function with differentcoefficients. At least some of the functions may approximate orillustrate a correlation between a physiological parameter of interestand the acquired signals (or some features of the acquired signals).Exemplary functions may include different polynomials, e.g., polynomialsof different degrees, polynomials of the same degree with differentcoefficients, or the like, or a combination thereof. For example, underon specific condition of the localized analysis, only the calibrationvalues (C) occurred within an interval may be considered as suggested inEquation 1:{C=(PTT₀,Blood Pressure₀)|PTT−a<PTT₀<PTT+b}.  Equation 1

In some embodiments, constants a and b in Equation 1 may be pre-definedindependently of a specific measurement. In some embodiments, constantsa and b in Equation 1 may be determined for a specific measurement. Theconstants may be determined based on, e.g., the acquired information andthe physiological parameter of interest (e.g., the blood pressure), fromthe subject, or from other subjects (e.g., a sub-group of a generalpopulation). The sub-group may share a same or similar characteristicincluding, for example, age, gender, nation, stature, weight, a body fatpercentage, color of skin, a family health history, a life style, anexercise habit or other habit, diet, occupation, illness history,education background, marital status, religious belief, or the like, orany combination thereof. The value of a and the value of b may bespecified by a subject, a user other than the subject, the system 100,or the like.

In one example, the measured PTT is 1 second, and only one or more setsof calibration values (C) with a PTT₀ value falling within the rangefrom 1-a second and 1+b second may be considered. The value of a and thevalue of b may be the same or different. Merely by a way of example, thevalue of a is factor1*PTT, the value of b is factor2*PTT. The factor1and factor2 may be any number in the range of (0, 1). In someembodiments, factor1 or factor2 may be 2%, or 5%, or 8%, or 10%, or 12%,or 15%, or 20%, or 25%, or larger than 25%. In some embodiments, factor1or factor2 may be lower than 50%, or lower than 40%, or lower than 30%,or lower than 25%, or lower than 20%, or lower than 15%, or lower than12%, or lower than 10%, or lower than 8%, or lower than 5%. Factor1 andfacor2 may be the same or different.

More descriptions regarding methods, models, calibration data forcalculating and calibrating the physiological parameter of interest maybe found in International Patent Application No. PCT/CN2015/083334 filedJul. 3, 2015, which is hereby incorporated by reference.

While the foregoing has described what are considered to constitute thepresent disclosure and/or other examples, it is understood that variousmodifications may be made thereto and that the subject matter disclosedherein may be implemented in various forms and examples, and that thedisclosure may be applied in numerous applications, only some of whichhave been described herein. Those skilled in the art will recognize thatpresent disclosure are amenable to a variety of modifications and/orenhancements. For example, the pre-treatment step 720 may beunnecessary. Additionally, a third signal may be acquired if needed, andthe third signal may be a signal with the same type with the firstsignal or the second signal, or may be a signal different with the firstsignal or the second signal.

FIG. 8-A through FIG. 8-E provide an exemplary signal processingaccording to some embodiments of the present disclosure. The one or moresecond signals mentioned in FIG. 7 may be analyzed and processed. Theanalysis and process may be performed by the analysis module 220. Thesignals (S) may be acquired in step 801. The acquisition may beperformed by the information acquisition module 210. The signals (S) tobe processed may be multiple PPG signals, multiple series of PPGsignals, multiple blood oxygen data, multiple series of blood oxygendata, multiple body temperature data, multiple series of bodytemperature data, or the like, or a combination thereof. As used herein,a series of data refers to a group or a set of data detected from aspecific body location of the subject. It is to say that multiple seriesof PPG signals refers to multiple group of PPG signals detected frommultiple body locations of the subject.

Then whether to select one or more body locations where signals are tobe acquired may be determined in step 802. If the answer is “yes”, oneor more body locations may be selected in step 807 and the signalsdetected from the selected body location(s) may be accessed or acquiredin step 808. After the signals are accessed or acquired, at least somesteps starting from node A 809 as illustrated in FIG. 8-B may beperformed.

If no body locations are selected, a plurality of signals detected frommultiple body locations may be loaded in step 803. The signals may beloaded from the information acquisition module 210, the server 120, thestorage unit 480, a storage device anywhere disclosed in the presentdisclosure, or the like, or a combination thereof. In some embodiments,the signals may include multiple PPG signals detected by multiplesensors placed on multiple body locations. For example, multiple PPGsignals may be detected by multiple sensors placed on the left arm, theright arm, the left leg, the right leg, or the like, or a combinationthereof. In some embodiments, the signals may include multiple series ofPPG signals detected by multiple sensor arrays placed on multiple bodylocations. For example, the signals may include a series of PPG signalsdetected by a sensor array placed on a body location and another seriesof PPG signals detected by another sensor array placed on another bodylocation, or the like, or a combination thereof. Similarly, the signalsmay include blood oxygen data, body temperature data or otherphysiological data detected from multiple body locations, or the like,or a combination thereof. The multiple body locations may include thehead, the neck, the chest, the abdomen, the upper arm, the wrist, thewaist, the upper leg, the knee, the ankle, or the like, or a combinationthereof.

At least some of the plurality of signals may be processed to provide anew signal in step 804. The new signal may be a signal derived from theplurality of signals loaded in step 803. The new signal may be selectedfrom the plurality of signals loaded in step 803. The new signal may bea signal calculated based on the plurality of signals loaded in step803. Methods of processing may include analog to digital conversion,digital to analog conversion, statistical analysis, least square method,method of mean value, modeling method, linear function method, historythreshold iteration method, comparison method, inductive method, imagemethod, or the like, or a combination thereof.

In some embodiments, the plurality of PPG signals may be multiple seriesof PPG signals. The multiple series of PPG signals may first beconverted from analog signals to digital signals expressed as below.S _(n)(t _(i))→D _(n)(t _(i) ,A _(i)).  Equation 2

As used herein, S_(n)(t_(i)) refers to a PPG signal, D_(n)(t_(i),A_(i))refers to the corresponding converted digital signal, t_(i) refers to atime point, A_(i) refers to the amplitude of the corresponding datapoint of the time point.

Then the data points corresponding to a time point may be performed by amathematical operation (for example, an averaging operation), i.e., theamplitudes of the data points may be operated to a processed value. Themathematical operation process continues until the data points of allthe time points are processed. After the process is finished, theprocessed values may be converted from digital data to an analog signal.Thus the initial multiple series of PPG signals are fused to a final newPPG signal.

In some embodiments, multiple blood oxygen data or multiple bodytemperature data may be acquired from multiple body locations of thesubject. The multiple blood oxygen data may be processed (for example,averaged) to generate a new blood oxygen value. Similarly, the multiplebody temperature data may be processed to generate a new bodytemperature value.

A physiological parameter of interest may be calculated based on the newsignal in step 805. In some embodiments, the new signal may be a PPGsignal. A blood pressure may be calculated based on the PPG signal andan ECG signal. The ECG signal may be acquired by the ECG acquisitionunit 430. More descriptions regarding the method and process of theblood pressure calculation may be found in International PatentApplication No. PCT/CN2015/083334 filed Jul. 3, 2015. The calculatedphysiological parameter of interest, the acquired signals (S), orphysiological data of the subject may be uploaded to personal healthmanager 900 in step 806. The personal health manager 900 may bedescribed in detail in FIG. 9.

FIG. 8-B illustrates the process starting from node A 809 regarding asignal processing of a body location according to some embodiments ofthe present disclosure. A body location of the subject may be determinedin step 810. The body location may be the head, the neck, the chest, theabdomen, the upper arm, the wrist, the waist, the upper leg, the knee,the ankle, or the like, or a combination thereof. Signals detected fromthe determined body location may be loaded in step 811. For thedetermined body location, a series of signals may be acquired by asensor array. After the body location is determined and the signals areloaded, a reference signal may be set in step 812. In some embodiments,the reference signal may be a real physiological signal, or may be avirtual simulated signal. In some embodiments, the reference signal maybe an integral signal, or may be a portion of an integral signal. Insome embodiments, the reference signal may be an analog signal, or maybe a digital signal. In some embodiments, the reference signal may be awaveform, or may be one or more data points. In some embodiments, thereference signal may be an accurate signal waveform, or may be aschematic outline. In some embodiments, the reference signal may be anindependent signal, or may be a data range. In some embodiments, thereference signal may be a time domain signal, or may be a frequencydomain signal. In some embodiments, the reference signal may be a signaldetected from the subject, or may be a signal detected from a subject ofa peer group. As used herein, the peer group is defined as a group ofpeople sharing at least some same or similar characteristics, forexample, same gender, similar age, similar height, similar weight,similar arm length, similar illness history, or the like, or acombination thereof.

A comparison between the reference signal and the loaded signal(s) maybe performed in step 813. The comparison may be with respect to thewaveform of the reference signal and the waveform of the loadedsignal(s). The comparison may be with respect to a feature of thereference signal and a corresponding feature point of the loadedsignal(s). Exemplary features may include the peak, the valley, the timereaching the peak or valley, frequency, or the like, or a combinationthereof. The comparison may provide a comparison result. The comparisonresult may be provided in the form of, for example, text, a graph, athree dimensional image, a code, a voice message, video, an audio alert,a haptic effect, or the like, or a combination thereof.

In step 814, a determination may be made as to whether the signals aresatisfied a condition (for example, a condition relating to a threshold)based on the comparison result generated in step 813. The condition maybe pre-determined or dynamically provided. In some embodiments, thecondition may be constant or variable. For instance, the condition maybe a predetermined profile as a function of, for example, the subject'sage, gender, height, body weight, or the like, or a combination thereof.The determination may be made by the system or a portion thereof (forexample, based on an instruction provided by a subject, a user otherthan the subject, a third party, or an instruction or a rule derived bymachine learning of prior data, prior behaviors of the subject, or of auser other than the subject), or provided by the subject or by a userother than the subject, or by a third party related to the subject (forexample, a doctor).

In step 814, the comparison result may be loaded and checked accordingto the condition. If the comparison result does not satisfy thecondition, the corresponding acquired signal may be abandoned in step815. Merely by way of example, in some embodiments, if the differencebetween the waveform of the acquired signal and the waveform of thereference signal does not satisfy a condition (for example, exceeds athreshold), the acquired signal may be abandoned. In some embodiments,compared with the reference signal, if a deviation of a data point ofthe acquired signal exceeds a threshold and therefore does not satisfy acondition, then the acquired signal may be abandoned. As used herein, adeviation may refer to the difference between the value of a data pointof the acquired signal with that of the reference signal. In someembodiments, compared with the reference signal, if a feature of theacquired signal does not match a corresponding feature of the referencesignal, the acquired signal may be determined to fail to satisfy acondition and may be abandoned. A signal(s) not abandoned based on, forexample, the comparison, may be referred to as a reserved signal. Thedescriptions above are only provided for illustration purposes, and notintended to limit the scope of the present disclosure.

In step 816, a data type conversion may be performed on the reservedsignals if needed. The data type conversion may be an analog to digitalconversion. In some embodiments, the reserved signal may be expressed asS(t), the converted signal may be expressed as D(t, A). The data typeconversion may be expressed as below.

                                      Equation  3 $\begin{matrix}{\left. {S_{1}(t)}\rightarrow{D_{1}\left( {t,A} \right)} \right. = \left\{ {{P_{11}\left( {t_{1},A_{11}} \right)},{P_{12}\left( {t_{2},A_{12}} \right)},\ldots\mspace{14mu},{P_{1\; m}\left( {t_{m},A_{1\; m}} \right)},} \right\}} \\{\left. {S_{2}(t)}\rightarrow{D_{2}\left( {t,A} \right)} \right. = \left\{ {{P_{21}\left( {t_{1},A_{21}} \right)},{P_{22}\left( {t_{2},A_{22}} \right)},\ldots\mspace{14mu},{P_{2\; m}\left( {t_{m},A_{2\; m}} \right)},} \right\}} \\\ldots \\{\left. {S_{n}(t)}\rightarrow{D_{n}\left( {t,A} \right)} \right. = \left\{ {{P_{n\; 1}\left( {t_{1},A_{n\; 1}} \right)},{P_{n\; 2}\left( {t_{2},A_{n\; 2}} \right)},\ldots\mspace{14mu},{P_{nm}\left( {t_{m},A_{nm}} \right)},} \right\}}\end{matrix}$

As used herein, the factor t may refer to a time point, the factor P mayrefer to the data point of the signal at the time point t, the factor Amay refer to the amplitude of the signal at the time point t. In someembodiments, if the reserved signal is a PPG signal, an analog todigital conversion may be performed. In some embodiments, if thereserved signal is a body temperature signal or a blood oxygen signal,then there is no need to perform a data type conversion on the reservedsignals. Using the PPG signal as an example, after the data typeconversion is performed, a PPG signal may be converted from a waveformsignal to a series of data points. Then it may follow at least somesteps starting from node B 817 as illustrated in FIG. 8-C.

FIG. 8-C illustrates the process starting from node B 817 regarding asignal process according to some embodiments of the present disclosure.In step 818, a series of data points expressed as a set D may beacquired from an analog signal by a data type conversion in step 816. Instep 819, a time point may be selected from a time point set T. The setD and the set T may be expressed as below.

$\begin{matrix}{{D = \begin{Bmatrix}\left\{ {{P_{11}\left( {t_{1},A_{11}} \right)},{P_{12}\left( {t_{2},A_{12}} \right)},\ldots\mspace{14mu},{P_{1\; m}\left( {t_{m},A_{1\; m}} \right)},} \right\} \\\left\{ {{P_{21}\left( {t_{1},A_{21}} \right)},{P_{22}\left( {t_{2},A_{22}} \right)},\ldots\mspace{14mu},{P_{2\; m}\left( {t_{m},A_{2\; m}} \right)},} \right\} \\\ldots \\\left\{ {{P_{n\; 1}\left( {t_{1},A_{n\; 1}} \right)},{P_{n\; 2}\left( {t_{2},A_{n\; 2}} \right)},\ldots\mspace{14mu},{P_{nm}\left( {t_{m},A_{nm}} \right)},} \right\}\end{Bmatrix}},} & {{Equation}\mspace{14mu} 4} \\{\mspace{79mu}{T = {\left\{ {t_{1},t_{2},\ldots\mspace{14mu},t_{m}} \right\}.}}} & {{Equation}\mspace{14mu} 5}\end{matrix}$

Then a data distribution of the corresponding series of data points maybe analyzed in step 820. Merely by way of example, if the time point t₁is selected, a distribution of the corresponding series of data points{P₁₁(t₁,A₁₁), P₂₁(t₁, A₂₁), . . . , Pn₁(t₁,An₁)} may be analyzed in step820. The analysis may be a statistical analysis, a mathematicalcalculation, a regression analysis, a cluster analysis, an analysis ofvariance, or the like, or a combination thereof. After the datadistribution analysis, a statistical result of the distribution of thedata points may be generated. The statistical result may be a tableformat including a series of data extent and corresponding percentage.The statistical result may be a data scatter plot (for example, ahistogram, a pie, a star chart, or the like). The statistical result maybe a mathematical expression. The statistical result may be amathematical model. The description above are only provided forillustration purposes, and not intended to limit the scope of thepresent disclosure. The statistical result also may be exhibited inother forms.

Then a threshold may be set in step 821. The threshold may be apercentage, an absolute value, a value range, or the like, or acombination thereof. In some embodiments, the threshold value expressedas v may be selected from an interval a<v<b. In some embodiments, thethreshold may be a value range expressed as a<v<b. The constants a and bmay be determined for a specific analysis. In some embodiments, theconstants may be a default value determined by the system or set by, forexample, a subject, a user other than the subject, a third party, or thelike, or a combination thereof. In some embodiments, the constants maybe determined based on the acquired information and the physiologicalparameter of interest (for example, the blood pressure) to becalculated, from the subject, or from other subjects (for example, asub-group of a general population). The sub-group may share a same orsimilar characteristic including, for example, age, gender, nation,stature, weight, a body fat percentage, color of skin, a family healthhistory, a life style, an exercise habit or other habit, diet,occupation, illness history, education background, marital status,religious belief, or the like, or any combination thereof. In someembodiments, the constants may be set by the subject, a user other thanthe subject, a related third party (for example, a doctor), or the like.

In step 822, a comparison may be performed to determine whether the datapoint exceeds the threshold regarding the distribution of the datapoints. If the answer is “yes,” the corresponding data point may beabandoned in step 823. Then the reserved data points may be performed bya mathematical operation in step 824. The mathematical operation may be,for example, a direct primary calculation including four rules ofarithmetic, a power operation, a rooting operation, or the like, or acombination thereof. The mathematical operation may be, for example, afunctional operation including a linear function, a quadratic function,a high order function, a polynomial, or the like, or a combinationthereof. The mathematical operation may be, for example, a simulationoperation including a physical simulation, a mathematical simulation, asemi-physical simulation, or the like, or a combination thereof. Thedescriptions above are only provides for illustration purposes, and notintended to limit the scope of present disclosure. Merely by way ofexample, the reserved data points may be performed by an averagingoperation which may be expressed as below.AVERAGE{P ₁₁(t ₁ ,A ₁₁),P ₂₁(t ₁ ,A ₂₁), . . . ,P _(n1)(t ₁ ,A_(n1))}.  Equation 6

After the mathematical operation, the system may proceed to step 819 toselect another time point and perform another data processing startingfrom step 820 until all the time points are selected and all thecorresponding data points are analyzed and processed. In step 825, theprocessed data may be performed by an inverse data type conversion if adata type conversion was performed in step 816. Then a new signal basedon the results of the above steps may be generated in step 826. Then itmay follow at least some steps starting from node C 827 as illustratedin FIG. 8-D.

FIG. 8-D illustrates the process starting from node C 827 regarding asignal process according to some embodiments of the present disclosure.Through the steps of processing starting from node B 817, a new signalmay be generated in step 826. A physiological parameter of interest maybe calculated based on the new signal in step 828. In some embodiments,the new signal may be a PPG signal. A blood pressure may be calculatedbased on the PPG signal and an ECG signal. The ECG signal may beacquired by the ECG acquisition unit 430 and stored in the storage unit480 or the server 120 or a storage device disclosed anywhere in thepresent disclosure. PTT may be determined based on the ECG signal andthe PPG signal. During the calculation of the physiological parameter ofinterest, a calibration process (not shown in FIG. 8-D) may beperformed. More descriptions regarding the method and process of thecalculation and the calibration may be found in International PatentApplication PCT/CN2015/083334 filed Jul. 3, 2015, the entire contents ofwhich are hereby incorporated by reference.

After the physiological parameter of interest is calculated, thenwhether to perform a comparison with data of other body locations may bechosen in step 829. The determination may be made by the system or aportion thereof (for example, based on an instruction provided by asubject, a user other than the subject, a third party, or an instructionor a rule derived by machine learning of prior data, prior behaviors ofthe subject, or of a user other than the subject), or provided by thesubject or by a user other than the subject. If the comparison ischosen, it may follow at least some steps starting from node D 830 asillustrated in FIG. 8-E.

If the comparison is not chosen, whether to perform a comparison withhistorical data may be determined in step 831. The determination may bemade by the system or a portion thereof (for example, based on aninstruction provided by a subject, a user other than the subject, or aninstruction or a rule derived by machine learning of prior data, priorbehaviors of the subject, or of a user other than the subject), orprovided by the subject or by a user other than the subject. In someembodiments, a variation from historical data may be calculated in step832 if the comparison determination is made. As used herein, thevariation may be determined based on a comparison between the calculatedphysiological parameter of interest and a historical data. Thehistorical data may be a physiological parameter of interest estimatedor measured before a time interval ago. The time interval may be anhour, a day, a week, a month, a year, or the like, or a combinationthereof. In some embodiments, a variation curve compared with historicaldata may be generated in step 832. As used herein, the variation curvemay be a curve representing the physiological parameter changing withtime within a time interval. Similarly, the time interval may be anhour, a day, a week, a month, a year, or the like, or a combinationthereof. The variation or the variation curve may be stored in theanalysis module 220 or the server 120 or personal health manager 900. Athreshold for a physiological parameter of interest may be set in step834. The threshold may be set by the subject, a user than the subject, athird party (for example, a doctor), the system default, or the like, ora combination thereof. The threshold may be an absolute value, a valuerange, a variation value between the current physiological parameter ofinterest with one or more historical data, or the like, or a combinationthereof. The threshold may be set based on the basic information of thesubject including age, gender, height, weight, illness history, or thelike, or a combination thereof. The threshold may be set based onhistorical data of the subject. The threshold may be set based on thestatistical data of a peer group. The threshold may be set based onempirical data of a group not limited to a peer group. Merely by way ofexample, a threshold for a blood pressure may set as below.DBPϵ{DBP_(a),DBP_(b)},SBPϵ{SBP_(a),SBP_(b)}.  Equation 7

For instance, for an ordinary person with normal blood pressure, thenormal DBP may be ≈120 mmHg (recorded as DBP_(N)), the normal SBP may be≈80 mmHg (recorded as SBP_(N)). The threshold DBP_(a), DBP_(b), SBP_(a)and SBP_(b) may be set as a certain percentage of the normal value. Forexample, DBP_(a) may be set as 90% DBP_(N) and DBP_(b) may be set as110% DBP_(N). Similarly, SBP_(a) and SBP_(b) may be set as 90% SBP_(N)and 110% SBP_(N), respectively. According to some embodiments of thepresent disclosure, the values of DBP_(a), DBP_(b), SBP_(a) and SBP_(b)may be personalized. And the values may vary under different situations(for example, when the subject is exercising or asleep, at differenttimes of a day, at the same or similar time on different days, or thelike, or a combination thereof.)

In step 834, a comparison between the physiological parameter ofinterest with the threshold may be performed. If the value of thephysiological parameter exceeds the threshold, or if the variationcalculated in step 832 exceeds the threshold, an alert may be generatedin step 835. The alert may include information relating to the subject,the threshold, the event that has triggered the alert, the time when theevent occurred, relevant historic information (for example, thefrequency that similar events have occurred, the occurrence of similarevents within a time period, etc.), the environmental information, orsome other related information. The information related to the subjectmay include the value of the physiological parameter of interest, or thevariation or the variation curve generated in step 832. Then the alertmay be transferred to a related third party in step 836. The relatedthird party may be a doctor, a healthcare worker, a medical institution,a research facility, a peripheral device of the subject or a userwell-connected to the subject, or the like. If the value of thephysiological parameter is within the scope of the threshold value, orif the variation calculated in step 832 is within the scope of thethreshold value, the results may be uploaded to personal health managerin step 837. And then the system may return to the steps starting fromnode A 809 to determine another body location and perform the subsequentsignal/data processing.

FIG. 8-E illustrates the subsequent steps regarding a process and ananalysis of the calculated physiological parameters of interest ofmultiple body locations from node D 830 according to some embodiments ofthe present disclosure. The physiological parameters of interest ofmultiple body locations may be loaded in step 838. The physiologicalparameters of interest may be loaded from the analysis module 220, theserver 120, any storage device disclosed anywhere in the presentdisclosure, or the like, or a combination thereof. In some embodiments,the physiological parameter of interest may be the blood pressure of thesubject. A relation information among the physiological parameters ofinterest of multiple body locations may be generated in step 839. Insome embodiments, a relation information among the second signals orrelation information among the plurality of parameters determined instep 750 (e.g., PTT) may be generated. The form of the relationinformation may be text, a graph, a three dimensional image, a code, avoice message, video, an audio alert, a haptic effect, or the like, or acombination thereof. Merely by way of example, the relation informationmay be in the form of a curve, a histogram diagram, a data distributionof different body locations, a difference value between the calculatedphysiological parameters with a reference value (for example, thereference value being a physiological parameter of an ordinary person),information based on a comparison between the left side and the right ofthe body, a difference between a calculated physiological parameter ofone body location with that of another body location (for example, theblood pressure value of the arm and the blood pressure value of the leg)of a subject, or the like, or a combination thereof.

In step 840, the system may detect whether errors occur. The error maybe an abnormal value, an abnormal wave form or an abnormal changethereof, an asymmetry information between the left side and the rightside of the body of a subject, a deviation from a reference value, orthe like, or a combination thereof. If errors are detected, thecorresponding data point and/or the associated body location (forexample, the body location where the data point was acquired) may bemarked in step 843. Then it may proceed to node A 809 and follow atleast some of the steps starting from node A 809 as illustrated in FIG.8-B and the description thereof. That is, a new acquisition process andanalysis process regarding the marked body location may be performed.

If no errors are detected, the relation information may be analyzed instep 841. In some embodiments, the analysis may be a comparison withhistorical data for generating a variation result. In some embodiments,the analysis may be a statistical analysis for generating a statisticalresult regarding a comparison with data of a peer group (for example, agroup of people sharing same age and same gender, or the like) In someembodiments, the analysis may be a mathematical operation for generatinga three dimensional display of the relation information. After therelation information is analyzed, a guide information may be generatedbased on the analysis.

In some embodiments, the guide information may be a prompting messageregarding selection of calculation models which may be used to calculatethe physiological parameter of interest of the subject (moredescriptions regarding the calculation models may be found inInternational Patent Application No. PCT/CN2015/083334 filed Jul. 3,2015, the contents of which are hereby incorporated by reference).Merely by way of example, the relation information generated in step 839may be a distribution of blood pressure values of multiple bodylocations of the subject. For another example, the relation informationgenerated in step 839 may be an interrelation of the determinedplurality of parameters (e.g., PTTs) or the acquired plurality of secondsignals. In some embodiments, guide information based on thedistribution of blood pressure values of multiple body locations may beprovided to the system. Then the system may select a favorite model or aproper model for a specific subject to calculate blood pressure during anext calculation process. For example, blood pressure distributions ofdifferent subjects may be different, or blood pressure distributions ofa specific subject may be different under different situations (e.g.,the blood pressure distribution may vary with environment temperature,air humidity, or the like). The selection of calculation models may beperformed based on an analysis result of historical data, or may beperformed based on a preset condition (e.g., different blood pressuredistributions may correspond to different calculation models). In someembodiments, guide information based on the interrelation of thedetermined plurality of parameters or the acquired plurality of secondsignals may be provided to the system. Then the system may select afavorite model or a proper model for a specific subject to calculateblood pressure before a calculation process.

In some embodiments, the guide information may be a modified model usedfor calculating the physiological parameter of interest of the subject.Merely by way of example, an initial model used for calculating bloodpressure of body location 1 may be expressed in Equation 8 according tothe description in the International Patent Application No.PCT/CN2015/083334 filed Jul. 3, 2015, the contents of which are herebyincorporated by reference.BP₁ =f(PTT₁),  Equation 8

In some embodiments. while the relation information of the bloodpressure value of the body location 1 with that of one or more otherbody locations is analyzed (for example, the relation informationbetween the blood pressure value of the arm and that of the leg, or therelation information among the blood pressure value of the head andthose of the arm, the leg, the wrist, or the like), a compensation termmay be generated based on the analysis result. The compensation term maybe a function of the relation information. The function form may bebased on a feature of the subject (for example, height, weight, gender,historical data, etc.). The compensation term may be added to theinitial model to generate a modified model in Equation 9. The guideinformation regarding a modified calculation model may increase thecalculation accuracy.BP₁ =f(PTT₁)+f _(compensation),  Equation 9

In some embodiments, while the relation information among the determinedplurality of parameters or the acquired plurality of second signals maybe analyzed, a compensation term may be generated based on the analysis.

In some embodiments, the guide information may be a push informationregarding daily activities. Exemplary information may include dieteticvarieties, water intake, sugar intake, sleep time, work and rest,duration and type of exercise, exercise intensity, or the like, or acombination thereof. In some embodiments, the guide information may bean information for reference provided to a related third party (forexample, a doctor, a healthcare worker, a medical institution, aresearch facility, or the like, or a combination thereof). For example,the blood related information of different body locations of the subjectmay be considered as a guide or a reference for a blood related surgery(for example, the determination of the surgery spot, or whether needsanesthesia, or the like). For another example, the physiologicalparameters of different body locations of the subject may be consideredas a guide or a reference for a medicine treatment (for example, typeand dose of the drugs, medication time, oral or injectable, or thelike). The guide information may be uploaded to the personal healthmanager 900 in step 844. The person health manager 900 may push theguide information to, for example, the subject, a user other than thesubject, a third party, or the like, or a combination thereof. The guideinformation may be viewed by, for example, the subject, a user otherthan the subject, a third party, or the like, or a combination thereof.The guide information may be accessible from, for example, a userinterface provided by the system.

FIG. 9 is an example of the composition and organization of the personalhealth manager 900 according to some embodiments of the presentdisclosure. The personal health manager 900 may be stored in the server120, locally on a measuring device 110, a terminal 140 connected orcommunicated with the system, or the like, or a combination thereof. Forinstance, the personal health manager 900 may be stored in a device of athird party. The personal health manager 900 may be displayed on aterminal 140, a display device of the system (not shown), a displaydevice of a third party, or the like, or a combination thereof. Thepersonal health manager 900 may have different sections including basicinformation 901, external environmental information 902, the status orof the physiological parameters of interest of different body locations903, variations of physiological parameters of interest with time 904,health tips 905, reference information provided for a related thirdparty 906, or other related sections.

The basic information 901 may include age, gender, height, weight,illness history, or the like, or a combination thereof. The externalenvironmental information 902 may include temperature, humidity, airquality, ultraviolet intensity, or the like, or a combination thereof.The basic information and the external environmental information may beacquired by the information acquisition module 210. The information maybe updated at a certain frequency (for example, once an hour, once aday, twice an hour, twice a day, three times an hour, three times a day,or the like). The information may be displayed selectively based on anoption or an instruction of the subject, or may be based on the systemdefault.

A status of a physiological parameter of interest may be displayed inthe section 903. The physiological parameter of interest may includeblood pressure, blood oxygen, heart rate, HRV, body temperature, or thelike, or a combination thereof. The subject, a user other than thesubject, or a related third party may choose date and time to review thestatus of the physiological parameters. The status level may includeexcellent, good, fair and poor. The different rectangular columnsrepresent different locations including the head, the neck, the chest,the abdomen, the upper arm, the wrist, the waist, the upper leg, theknee, the ankle, or the like, or a combination thereof. Through thesection 903, the status of the physiological parameters of interest ofthe subject on different body locations may be displayed vividly in realtime. And also historical information may be displayed based on anoption of the subject regarding date and time.

A variation of a physiological parameter of interest may be displayed insection 904. The subject, a user other than the subject, or a relatedthird party may choose a time interval and a body location, then thevariations of the physiological parameters of interest may be displayed(as is symbolically illustrated in FIG. 9). The time interval may be anhour, a day, a week, a month, two months, or the like, or a combinationthereof. The subject, a user other than the subject, or a related thirdparty may click the icons to see the variation curves of differentphysiological parameters of interest. The health tips section 905 mayinclude information regarding sleep, diet, exercises, or the like, or acombination thereof. The health tips may be retrieved from the server120 based on the basic information of the subject, the calculatedphysiological parameters of interest, the variations, or the like, or acombination thereof. A related third party (for example, a doctor, ahealthcare worker, a medical institution, a research facility, aperipheral device of the subject or a user well-connected to thesubject, or the like) may input some health tips in the section 905.Similarly, the subject or a user other than the subject also may input amemo list regarding sleep, diet, exercises, or the like. The subject maycustomize an information push regarding health tips based on an optionor an instruction.

Reference information for a related third party may be displayed insection 906. The related third party may be a doctor, a healthcareworker, a medical institution, a research facility, or the like, or acombination thereof. The reference information may be generated based onthe status of the calculated physiological parameters of interest, thebasic information of the subject, or both. The reference information mayinclude information regarding determination of surgery spot, or whetherneeds anesthesia, or type and dose of drugs, or the like, or acombination thereof. The reference information may provide some guidanceto the related third party at a right moment.

FIG. 10 depicts the architecture of a mobile device that may be used torealize a specialized system implementing the present disclosure. Inthis example, the device (for example, the terminal 140) on whichinformation relating to blood pressure monitoring is presented andinteracted-with is a mobile device 1000, including, but is not limitedto, a smart phone, a tablet, a music player, a handled gaming console, aglobal positioning system (GPS) receiver, and a wearable computingdevice (for example, eyeglasses, wrist watch, etc.), or in any otherform factor. The mobile device 1000 in this example includes one or morecentral processing units (CPUs) 1040, one or more graphic processingunits (GPUs) 1030, a display 1020, a memory 1060, a communicationplatform 1010, such as a wireless communication module, storage 1090,and one or more input/output (I/O) devices 1050. Any other suitablecomponent, including a system bus or a controller (not shown), may alsobe included in the mobile device 1000. As shown in FIG. 10, a mobileoperating system 1070, for example, iOS, Android, Windows Phone, etc.,and one or more applications 1080 may be loaded into the memory 1060from the storage 1090 in order to be executed by the CPU 1040. Theapplications 1080 may include a browser or any other suitable mobileapps for receiving and rendering information relating to blood pressuremonitoring or other information from the engine 200 on the mobile device1000. User interactions with the information stream may be achieved viathe I/O devices 1050 and provided to the engine 200 and/or othercomponents of system 100, for example, via the network 150.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein (for example, the engine 200, and/or other components of thesystem 100 described with respect to FIGS. 1-9 and 12). The hardwareelements, operating systems and programming languages of such computersare conventional in nature, and it is presumed that those skilled in theart are adequately familiar therewith to adapt those technologies to theblood pressure monitoring as described herein. A computer with userinterface elements may be used to implement a personal computer (PC) orother type of work station or terminal device, although a computer mayalso act as a server if appropriately programmed. It is believed thatthose skilled in the art are familiar with the structure, programmingand general operation of such computer equipment and as a result thedrawings should be self-explanatory.

FIG. 11 depicts the architecture of a computing device that may be usedto realize a specialized system implementing the present disclosure.Such a specialized system incorporating the present teaching has afunctional block diagram illustration of a hardware platform thatincludes user interface elements. The computer may be a general purposecomputer or a special purpose computer. Both may be used to implement aspecialized system for the present disclosure. This computer 1100 may beused to implement any component of the blood pressure monitoring asdescribed herein. For example, the engine 200, etc., may be implementedon a computer such as computer 1100, via its hardware, software program,firmware, or a combination thereof. Although only one such computer isshown, for convenience, the computer functions relating to the bloodpressure monitoring as described herein may be implemented in adistributed fashion on a number of similar platforms, to distribute theprocessing load.

The computer 1100, for example, includes COM ports 1150 connected to andfrom a network connected thereto to facilitate data communications. Thecomputer 1100 also includes a central processing unit (CPU) 1120, in theform of one or more processors, for executing program instructions. Theexemplary computer platform includes an internal communication bus 1110,program storage and data storage of different forms, for example, disk1170, read only memory (ROM) 1130, or random access memory (RAM) 1140,for various data files to be processed and/or transmitted by thecomputer, as well as possibly program instructions to be executed by theCPU. The computer 1100 also includes an I/O component 1160, supportinginput/output between the computer and other components therein such asuser interface elements 1180. The computer 1100 may also receiveprogramming and data via network communications.

Hence, aspects of the methods of the blood pressure monitoring and/orother processes, as outlined above, may be embodied in programming.Program aspects of the technology may be thought of as “products” or“articles of manufacture” typically in the form of executable codeand/or associated data that is carried on or embodied in a type ofmachine readable medium. Tangible non-transitory “storage” type mediainclude any or all of the memory or other storage for the computers,processors, or the like, or associated modules thereof, such as varioussemiconductor memories, tape drives, disk drives and the like, which mayprovide storage at any time for the software programming.

All or portions of the software may at times be communicated through anetwork such as the Internet or various other telecommunicationnetworks. Such communications, for example, may enable loading of thesoftware from one computer or processor into another, for example, froma management server or host computer of the engine 200 into the hardwareplatform(s) of a computing environment or other system implementing acomputing environment or similar functionalities in connection with theblood pressure monitoring. Thus, another type of media that may bear thesoftware elements includes optical, electrical and electromagneticwaves, such as used across physical interfaces between local devices,through wired and optical land line networks and over various air-links.The physical elements that carry such waves, such as wired or wirelesslinks, optical links or the like, also may be considered as mediabearing the software. As used herein, unless restricted to tangible“storage” media, terms such as computer or machine “readable medium”refer to any medium that participates in providing instructions to aprocessor for execution.

Hence, a machine-readable medium may take many forms, including atangible storage medium, a carrier wave medium or physical transmissionmedium. Non-volatile storage media include, for example, optical ormagnetic disks, such as any of the storage devices in any computer(s) orthe like, which may be used to implement the system or any of itscomponents as shown in the drawings. Volatile storage media includedynamic memory, such as a main memory of such a computer platform.Tangible transmission media include coaxial cables; copper wire andfiber optics, including the wires that form a bus within a computersystem. Carrier-wave transmission media may take the form of electric orelectromagnetic signals, or acoustic or light waves such as thosegenerated during radio frequency (RF) and infrared (IR) datacommunications. Common forms of computer-readable media thereforeinclude for example: a floppy disk, a flexible disk, hard disk, magnetictape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any otheroptical medium, punch cards paper tape, any other physical storagemedium with patterns of holes, a RAM, a PROM and EPROM, a FLASH-EPROM,any other memory chip or cartridge, a carrier wave transporting data orinstructions, cables or links transporting such a carrier wave, or anyother medium from which a computer may read programming code and/ordata. Many of these forms of computer readable media may be involved incarrying one or more sequences of one or more instructions to a physicalprocessor for execution.

Those skilled in the art will recognize that the present disclosure areamenable to a variety of modifications and/or enhancements. For example,although the implementation of various components described above may beembodied in a hardware device, it may also be implemented as a softwareonly solution—for example, an installation on an existing server. Inaddition, the blood pressure monitoring system as disclosed herein maybe implemented as a firmware, firmware/software combination,firmware/hardware combination, or a hardware/firmware/softwarecombination.

EXAMPLE

The following example is provided for illustration purposes, and notintended to limit the scope of the present disclosure.

A system used for measuring blood pressure may include a testing device1200, peripheral equipment 240 and a server 120. FIG. 12 illustrates anexemplary testing device according to some embodiments of theembodiment. The testing device 1200 may include an informationacquisition module 1210, an analysis module 220′, a storage 1220, and acontrol unit 1230. The testing device 1200 may be connected or otherwisecommunicate with a terminal 1240. The information acquisition module1210 may include multiple sensor arrays arranged on multiple locationsof the testing device 1200.

According to the embodiment, the information acquisition module 1210 isconfigured for acquiring information, for example, an ECG signal, a PPGsignal, or the like, or a combination thereof. The analysis module 220′is configured for analyzing and processing the acquired information, ordetermining or estimating a physiological parameter (for example, thephysiological parameter of interest), or both. The storage 1220 isconfigured for storing the detected or acquired signal, thephysiological parameter, or the like, or a combination thereof. Thecontrol unit 1230 is configured for controlling the ON/OFF of the sensorarrays, the location of the sensor arrays, the arrangement of the sensorarrays, the information acquisition parameter configuration, or thelike, or a combination thereof. In the embodiment, the control unit 1230may be a stepper machine, or may be a micro-motor. According to theembodiment, the information acquisition module 1210 includes twoacquisition units (not shown in FIG. 12), an ECG acquisition unit and aPPG acquisition unit. The ECG acquisition unit is configured fordetecting an ECG signal. The PPG acquisition unit is configured fordetecting a PPG signal. The acquired ECG signal and the PPG signal maybe stored in the storage 1220, or in the server 120, or in the terminal1240, or the like, or a combination thereof.

The testing device 1200 may be a wearable device or may be a portabledevice. The testing device 1200 may be a coat-like device. A schematicdiagram of the coat-like device is illustrated in FIG. 12 (some detailshave been elided for brevity). It may be seen that multiple sensorarrays configured for detecting PPG signals are integrated into thecoat. The sensor array may include multiple photoelectric sensors, ormay include multiple receiving ends. One or more light sources (notshown in FIG. 12) may be integrated into the sensor array, or may beplaced in other devices, or may be arranged in the testing device 1200independently. The sensor array may be placed on the head, the chest,the upper arm, the wrist, or the like, or a combination thereof. Thesensor array may be an oval array, a rectangular array, a rhombus array,a circular array, or the like, or a combination thereof. In theembodiment, the sensor array is an oval array. The arrangement of thephotoelectric sensors within the sensor array shown in FIG. 12 is onlyprovides for illustration purposes, the amount and the arrangement ofthe sensors may be adjusted under different situations. Merely by way ofexample, the number and the arrangement of the sensors may be customizedbased on the subject's requirements. In one example, if the subjectwants to obtain a physiological information regarding an organ, thesensor array may be placed on a body location nearby the organ area. Inanother example, the arrangement of the sensors may be adjustedaccording the subject's body size. In another example, a memory metalmay be used for remembering the location and the arrangement of thesensor array to achieve personalization purpose.

Although not shown in FIG. 12, an ECG acquisition unit configured fordetecting ECG signals is integrated into the coat. The ECG acquisitionunit includes 10 electrodes (four placed on the chest, two placed on thetwo arms, two placed on the two legs). The 10 electrodes may constitute12 leads and an ECG signal may be detected. The detected ECG signals maybe stored in the storage 1220, or may be stored in the server 120, ormay be loaded by the analysis module 220′ for subsequent calculation.

The coat may also include some other additional components including aWIFI device, a blue tooth device, a NFC device, a GPS device, or thelike, or a combination thereof. For instance, the WIFI device may beused for linking to a wireless network. The blue tooth device may beused for data transformation among some wired or wireless terminalswithin a certain distance. The NFC device may be used to enableterminals establishing radio communication within a short distance (10cm or less). The GPS device may allow the subject to find his ownposition, or the GPS device may be used to navigate, or the like, or acombination thereof. The additional components may be connected orotherwise communicate with the information acquisition module 210, theanalysis module 220′, the control unit 1230, the terminal 1240, and theserver 120.

The coat may communicate with a healthcare provider located in alocation remote from the subject. The communication may be achieveddirectly by the coat, or indirectly via, for example, the terminal 1240carried by the subject. The physiological parameter, as well as locationinformation, of the subject may be transmitted to the healthcareprovider in real-time, periodically, or when a triggering event occurs.Exemplary trigger events are described elsewhere in the presentdisclosure. When an emergency occurs, for example, the physiologicalparameter exceeding a threshold, the healthcare provider may benotified, the subject may be located based on the positioninginformation from the GPS or location sensor, and medical services may beprovided accordingly.

The analysis module 220′ is configured for analyzing and processing adetected ECG signal, or multiple series of PPG signals detected bymultiple sensor arrays, or the like, or a combination thereof. Accordingto the embodiment, a series of PPG signals may be analyzed andprocessed. According to the embodiment, the sensor arrays may be placedon the chest, the abdomen, the upper arm, the wrist, the head, or thelike. The sensor may include a series of photoelectric sensors arrangedin an array (for example, an oval array, a rectangular array, or thelike). Take the sensor array placed on the head for example, a series ofPPG signals may be detected by the sensor array arranged in an ovalarray. The sensor array includes eleven (or more in other differentsituations) photoelectric sensors. Then eleven PPG signals may bedetected by the eleven sensors. The eleven PPG signals may be analyzedand processed by the analysis module 220′. The eleven PPG signals may becompared with a reference signal to screen out and abandon signal(s) notsatisfied the threshold condition. Then a data type conversion may beperformed on the reserved PPG signals. If no signals are abandoned, theneleven analog PPG signals are converted to eleven series of data points.Furthermore, a time point corresponds to eleven data points as expressedbelow.

$\begin{matrix}{\left\lbrack {t_{1}\text{:}} \middle| \begin{matrix}P_{1,1} \\P_{1,2} \\\ldots \\P_{1,11}\end{matrix} \right\rbrack,\left\lbrack {t_{2}\text{:}} \middle| \begin{matrix}P_{2,1} \\P_{2,2} \\\ldots \\P_{2,11}\end{matrix} \right\rbrack,\ldots\mspace{14mu},{\left\lbrack {t_{n}\text{:}} \middle| \begin{matrix}P_{n,1} \\P_{n,2} \\\ldots \\P_{n,11}\end{matrix} \right\rbrack.}} & {{Equation}\mspace{14mu} 10}\end{matrix}$

The eleven data points corresponding to a time point may be performed bya mathematical operation (for example, averaging) to generate a finaldata point. The final data points together with the time points may beperformed by an inverse data type conversion. A new PPG signal may begenerated. The new generated PPG signal together with the detected oracquired ECG signal may be used for calculating a physiologicalparameter of interest (for example, blood pressure).

The analysis may also may include pre-treatment, feature identification,parameter estimation, calibration, or the like, or a combinationthereof. More descriptions regarding the analysis may be found inInternational Patent Application No. PCT/CN2015/083334 filed Jul. 3,2015. The calculated physiological parameter of interest may be uploadedto personal health manager 900 in the server 120. The details may bedisplayed in the terminal 1240, or may be transmitted to a related thirdparty (for example, a medical institution).

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure. Inaddition, the term “logic” is representative of hardware, firmware,software (or any combination thereof) to perform one or more functions.For instance, examples of “hardware” include, but are not limited to, anintegrated circuit, a finite state machine, or even combinatorial logic.The integrated circuit may take the form of a processor such as amicroprocessor, an application specific integrated circuit, a digitalsignal processor, a micro-controller, or the like.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “circuit,” “unit,” “module,” “component,” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readable mediahaving computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or anysuitable combination thereof. A computer readable signal medium may beany computer readable medium that is not a computer readable storagemedium and that may communicate, propagate, or transport a program foruse by or in connection with an instruction execution system, apparatus,or device. Program code embodied on a computer readable signal mediummay be transmitted using any appropriate medium, including wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C #, VB.NET, Python or the like, conventional procedural programming languages,such as the “C” programming language, Visual Basic, Fortran 2003, Perl,COBOL 2002, PHP, ABAP, dynamic programming languages such as Python,Ruby and Groovy, or other programming languages. The program code mayexecute entirely on the user's computer, partly on the user's computer,as a stand-alone software package, partly on the user's computer andpartly on a remote computer or entirely on the remote computer orserver. In the latter scenario, the remote computer may be connected tothe user's computer through any type of network, including a local areanetwork (LAN) or a wide area network (WAN), or the connection may bemade to an external computer (for example, through the Internet using anInternet Service Provider) or in a cloud computing environment oroffered as a service such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution—for example, aninstallation on an existing server or mobile device. In addition, thefinancial management system disclosed herein may be implemented as afirmware, firmware/software combination, firmware/hardware combination,or a hardware/firmware/software combination.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

We claim:
 1. A device comprising: non-transitory computer readablememory storing instructions; and at least one processor that executesthe instructions to perform operations comprising: receiving a firstsignal representing heart activity of a subject; receiving a pluralityof second signals representing time-varying information on at least onepulse wave of the subject; identifying a first feature in the firstsignal; identifying, for each of the plurality of second signals, asecond feature; computing a pulse transit time based on a differencebetween the first feature and at least one of the second features; andcalculating a blood pressure of the subject according to a model basedon the computed pulse transit time, the model comprising a compensationterm relating to distribution or interrelation of the plurality ofsecond signals or the second features thereof.
 2. The device of claim 1,the operations further comprising selecting the model based on theplurality of the second signals or the second features thereof.
 3. Thedevice of claim 1, the receiving the first signal comprisingcommunicating with a first sensor configured to acquire the first signalat a first location on the body of the subject.
 4. The device of claim1, the receiving the plurality of second signals comprisingcommunicating with a plurality of second sensors configured to acquirethe plurality of second signals at a plurality of second locations onthe body of the subject.
 5. The device of claim 4, at least one of theplurality of second sensors comprising a sensor array including aplurality of sensors, a plurality of receiving ends of one or moresensors, or a plurality of emitting ends of one or more sensors.
 6. Thedevice of claim 5, the sensor array having a configuration of an ovalarray, a rectangular array, a circular array, or a triangular array. 7.The device of claim 4, the plurality of second locations comprising atleast one location selected from the head, the neck, the chest, theabdomen, the upper arm, the wrist, the waist, the upper leg, the knee,or the ankle of the subject.
 8. The device of claim 4, the operationsfurther comprising: calculating a plurality of blood pressures of theplurality of second locations on the body of the subject.
 9. The deviceof claim 8, the operations further comprising: providing, based on theplurality of calculated blood pressures, a recommendation relating tothe subject.
 10. The device of claim 1, at least one of the first signalor the plurality of second signals comprising an optical signal or anelectrical signal.
 11. The device of claim 1, the operations furthercomprising receiving a set of calibration values, and the calculatingthe blood pressure comprising using the set of calibration values. 12.The device of claim 1, at least one of the first signal or the pluralityof second signals comprising a PPG waveform, an ECG waveform, or a BCGwaveform.
 13. The device of claim 1, wherein the first feature of thefirst signal corresponds to a first time point; and the identifying, foreach of the plurality of second signals, the second feature comprises:selecting a segment of each of the second signals, the segment occurringwithin a time window from the first time point; and locating the secondfeature corresponding to a second time point in the segment.
 14. Amethod comprising: receiving a first signal representing a heartactivity of a subject; receiving a plurality of second signalsrepresenting time-varying information on at least one pulse wave of thesubject; identifying a first feature in the first signal; identifying,for each of the plurality of second signals, a second feature; computinga pulse transit time based on a difference between the first feature andat least one of the second features; and calculating a blood pressure ofthe subject according to a model based on the computed pulse transittime, the model comprising a compensation term relating to distributionor interrelation of the plurality of second signals or the secondfeatures thereof.
 15. The method of claim 14, further comprisingacquiring the first signal at a first location on the body of thesubject.
 16. The method of claim 14, further comprising acquiring theplurality of second signals at a plurality of second locations on thebody of the subject.
 17. The method of claim 16, the plurality of secondlocations comprising at least one location selected from the head, theneck, the chest, the abdomen, the upper arm, the wrist, the waist, theupper leg, the knee, or the ankle of the subject.
 18. The method ofclaim 16, further comprising calculating a plurality of blood pressuresof the plurality of second locations on the body of the subject.
 19. Themethod of claim 14, further comprising receiving a set of calibrationvalues, and the calculating the blood pressure comprising using the setof calibration values.
 20. The method of claim 14, wherein the firstfeature of the first signal corresponds to a first time point; and theidentifying, for each of the plurality of second signals, the secondfeature comprises: selecting a segment of each of the second signals,the segment occurring within a time window from the first time point;and locating the second feature corresponding to a second time point inthe segment.